problem solving and program development steps

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A guide to problem-solving techniques, steps, and skills

You might associate problem-solving with the math exercises that a seven-year-old would do at school. But problem-solving isn’t just about math — it’s a crucial skill that helps everyone make better decisions in everyday life or work.

Problem-solving involves finding effective solutions to address complex challenges, in any context they may arise.

Unfortunately, structured and systematic problem-solving methods aren’t commonly taught. Instead, when solving a problem, PMs tend to rely heavily on intuition. While for simple issues this might work well, solving a complex problem with a straightforward solution is often ineffective and can even create more problems.

In this article, you’ll learn a framework for approaching problem-solving, alongside how you can improve your problem-solving skills.

The 7 steps to problem-solving

When it comes to problem-solving there are seven key steps that you should follow: define the problem, disaggregate, prioritize problem branches, create an analysis plan, conduct analysis, synthesis, and communication.

1. Define the problem

Problem-solving begins with a clear understanding of the issue at hand. Without a well-defined problem statement, confusion and misunderstandings can hinder progress. It’s crucial to ensure that the problem statement is outcome-focused, specific, measurable whenever possible, and time-bound.

Additionally, aligning the problem definition with relevant stakeholders and decision-makers is essential to ensure efforts are directed towards addressing the actual problem rather than side issues.

2. Disaggregate

Complex issues often require deeper analysis. Instead of tackling the entire problem at once, the next step is to break it down into smaller, more manageable components.

Various types of logic trees (also known as issue trees or decision trees) can be used to break down the problem. At each stage where new branches are created, it’s important for them to be “MECE” – mutually exclusive and collectively exhaustive. This process of breaking down continues until manageable components are identified, allowing for individual examination.

The decomposition of the problem demands looking at the problem from various perspectives. That is why collaboration within a team often yields more valuable results, as diverse viewpoints lead to a richer pool of ideas and solutions.

3. Prioritize problem branches

The next step involves prioritization. Not all branches of the problem tree have the same impact, so it’s important to understand the significance of each and focus attention on the most impactful areas. Prioritizing helps streamline efforts and minimize the time required to solve the problem.

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4. Create an analysis plan

For prioritized components, you may need to conduct in-depth analysis. Before proceeding, a work plan is created for data gathering and analysis. If work is conducted within a team, having a plan provides guidance on what needs to be achieved, who is responsible for which tasks, and the timelines involved.

5. Conduct analysis

Data gathering and analysis are central to the problem-solving process. It’s a good practice to set time limits for this phase to prevent excessive time spent on perfecting details. You can employ heuristics and rule-of-thumb reasoning to improve efficiency and direct efforts towards the most impactful work.

6. Synthesis

After each individual branch component has been researched, the problem isn’t solved yet. The next step is synthesizing the data logically to address the initial question. The synthesis process and the logical relationship between the individual branch results depend on the logic tree used.

7. Communication

The last step is communicating the story and the solution of the problem to the stakeholders and decision-makers. Clear effective communication is necessary to build trust in the solution and facilitates understanding among all parties involved. It ensures that stakeholders grasp the intricacies of the problem and the proposed solution, leading to informed decision-making.

Exploring problem-solving in various contexts

While problem-solving has traditionally been associated with fields like engineering and science, today it has become a fundamental skill for individuals across all professions. In fact, problem-solving consistently ranks as one of the top skills required by employers.

Problem-solving techniques can be applied in diverse contexts:

• Individuals — What career path should I choose? Where should I live? These are examples of simple and common personal challenges that require effective problem-solving skills
• Organizations — Businesses also face many decisions that are not trivial to answer. Should we expand into new markets this year? How can we enhance the quality of our product development? Will our office accommodate the upcoming year’s growth in terms of capacity?
• Societal issues — The biggest world challenges are also complex problems that can be addressed with the same technique. How can we minimize the impact of climate change? How do we fight cancer?

Despite the variation in domains and contexts, the fundamental approach to solving these questions remains the same. It starts with gaining a clear understanding of the problem, followed by decomposition, conducting analysis of the decomposed branches, and synthesizing it into a result that answers the initial problem.

Real-world examples of problem-solving

Let’s now explore some examples where we can apply the problem solving framework.

Problem: In the production of electronic devices, you observe an increasing number of defects. How can you reduce the error rate and improve the quality?

Before delving into analysis, you can deprioritize branches that you already have information for or ones you deem less important. For instance, while transportation delays may occur, the resulting material degradation is likely negligible. For other branches, additional research and data gathering may be necessary.

Once results are obtained, synthesis is crucial to address the core question: How can you decrease the defect rate?

While all factors listed may play a role, their significance varies. Your task is to prioritize effectively. Through data analysis, you may discover that altering the equipment would bring the most substantial positive outcome. However, executing a solution isn’t always straightforward. In prioritizing, you should consider both the potential impact and the level of effort needed for implementation.

By evaluating impact and effort, you can systematically prioritize areas for improvement, focusing on those with high impact and requiring minimal effort to address. This approach ensures efficient allocation of resources towards improvements that offer the greatest return on investment.

Problem : What should be my next job role?

When breaking down this problem, you need to consider various factors that are important for your future happiness in the role. This includes aspects like the company culture, our interest in the work itself, and the lifestyle that you can afford with the role.

However, not all factors carry the same weight for us. To make sense of the results, we can assign a weight factor to each branch. For instance, passion for the job role may have a weight factor of 1, while interest in the industry may have a weight factor of 0.5, because that is less important for you.

By applying these weights to a specific role and summing the values, you can have an estimate of how suitable that role is for you. Moreover, you can compare two roles and make an informed decision based on these weighted indicators.

Key problem-solving skills

This framework provides the foundation and guidance needed to effectively solve problems. However, successfully applying this framework requires the following:

• Creativity — During the decomposition phase, it’s essential to approach the problem from various perspectives and think outside the box to generate innovative ideas for breaking down the problem tree
• Decision-making — Throughout the process, decisions must be made, even when full confidence is lacking. Employing rules of thumb to simplify analysis or selecting one tree cut over another requires decisiveness and comfort with choices made
• Analytical skills — Analytical and research skills are necessary for the phase following decomposition, involving data gathering and analysis on selected tree branches
• Teamwork — Collaboration and teamwork are crucial when working within a team setting. Solving problems effectively often requires collective effort and shared responsibility
• Communication — Clear and structured communication is essential to convey the problem solution to stakeholders and decision-makers and build trust

How to enhance your problem-solving skills

Problem-solving requires practice and a certain mindset. The more you practice, the easier it becomes. Here are some strategies to enhance your skills:

• Practice structured thinking in your daily life — Break down problems or questions into manageable parts. You don’t need to go through the entire problem-solving process and conduct detailed analysis. When conveying a message, simplify the conversation by breaking the message into smaller, more understandable segments
• Regularly challenging yourself with games and puzzles — Solving puzzles, riddles, or strategy games can boost your problem-solving skills and cognitive agility.
• Engage with individuals from diverse backgrounds and viewpoints — Conversing with people who offer different perspectives provides fresh insights and alternative solutions to problems. This boosts creativity and helps in approaching challenges from new angles

Final thoughts

Problem-solving extends far beyond mathematics or scientific fields; it’s a critical skill for making informed decisions in every area of life and work. The seven-step framework presented here provides a systematic approach to problem-solving, relevant across various domains.

Now, consider this: What’s one question currently on your mind? Grab a piece of paper and try to apply the problem-solving framework. You might uncover fresh insights you hadn’t considered before.

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What Are the the Steps in Program Development?

Program development is a critical process that drives innovation and problem-solving in today’s technology-driven world. Whether you’re a seasoned developer or just starting, understanding the steps in program development is essential. In this article, we will walk you through the entire process, providing valuable insights and expert guidance. Let’s dive into “What are the steps in Program Development?”

Table of Contents

1. Understanding the Problem

Before you embark on any program development journey, it’s crucial to comprehend the problem you’re trying to solve. This initial step sets the foundation for the entire process. Take time to analyze the problem thoroughly, gather requirements, and define your objectives.

2. Research and Planning

Once you’ve identified the problem, research becomes your best friend. Dive deep into existing solutions, market trends, and user expectations. Create a detailed plan that outlines project goals, timelines, and resource allocation.

3. Designing the Solution

Now that you have a clear plan, it’s time to design your program. This step involves creating a system architecture, user interfaces, and database structures. Pay close attention to scalability and user experience during this phase.

4. Coding and Development

The heart of program development lies in coding. Skilled developers write the actual code based on the design specifications. This phase demands attention to detail and adherence to coding standards.

5. Testing and Quality Assurance

No program is complete without rigorous testing. Quality assurance ensures that the program functions as intended and is free from bugs and errors. Various testing methodologies, such as unit testing, integration testing, and user acceptance testing, come into play here.

6. Review and Feedback

After initial testing, gather feedback from stakeholders and users. This iterative process helps in refining the program and making necessary adjustments.

7. Documentation

Documenting your program is often overlooked but is crucial for future reference and maintenance. Create comprehensive documentation that includes user guides, code comments, and system architecture diagrams.

8. Deployment

Once your program is polished and fully tested, it’s time to deploy it in a production environment. Ensure a smooth transition from development to live usage.

9. Monitoring and Maintenance

Even after deployment, the work doesn’t stop. Continuous monitoring helps in identifying and addressing issues as they arise. Regular maintenance ensures the program remains up-to-date and secure.

10. User Training and Support

Provide training to end-users and offer ongoing support. User satisfaction is a key indicator of the success of your program.

11. Scaling and Optimization

As your program gains traction, you may need to scale it to accommodate more users and data. Optimization efforts should focus on improving performance and efficiency.

12. Security

Security should be a top priority throughout the development process. Implement robust security measures to protect user data and the integrity of your program.

13. Compliance and Regulations

Depending on the nature of your program, you may need to comply with industry-specific regulations and standards. Ensure that your program meets all legal requirements.

14. Feedback Loop

Maintain an open feedback loop with users and stakeholders to continuously improve your program.

15. Data Backup and Recovery

Implement robust data backup and recovery mechanisms to safeguard against data loss.

16. User Experience Enhancement

Regularly gather user feedback to enhance the user experience and make necessary updates.

17. Adaptation to Technological Advancements

Stay updated with the latest technology trends and adapt your program to incorporate relevant advancements.

18. Cross-Platform Compatibility

If applicable, ensure that your program works seamlessly across different platforms and devices.

19. Data Analytics and Reporting

Leverage data analytics to gain insights into user behavior and program performance. Create meaningful reports for stakeholders.

20. User Feedback Integration

Integrate user feedback into future updates and versions of your program.

21. User Engagement Strategies

Implement strategies to keep users engaged and active within your program.

22. Market Analysis

Continuously analyze the market to identify opportunities for improvement and expansion.

23. Competitor Analysis

Study your competitors to stay ahead in the industry and adapt your program accordingly.

24. Resource Management

Optimize resource allocation to ensure efficiency in program development.

25. Future Planning

Plan for the long-term sustainability and growth of your program.

Frequently Asked Questions (FAQs)

What are the key challenges in program development.

Program development often faces challenges such as changing requirements, tight deadlines, and budget constraints. Effective communication and project management are essential to overcome these challenges.

How long does it take to develop a program?

The timeline for program development varies widely depending on complexity and scope. Small projects may take a few weeks, while large-scale applications can take several months or even years.

Is coding the most critical step in program development?

While coding is a crucial step, every phase of program development is essential. Planning, design, testing, and maintenance are equally important for a successful program.

What programming languages are commonly used in program development?

Commonly used programming languages include Python, Java, C++, and JavaScript. The choice of language depends on the specific requirements of the project.

How can I ensure the security of my program?

To ensure program security, follow best practices such as code reviews, penetration testing, and regular updates to address vulnerabilities.

What are the benefits of agile development in program development?

Agile development promotes flexibility, collaboration, and rapid iteration, making it well-suited for dynamic and evolving projects.

Program development is a multifaceted process that demands careful planning, meticulous execution, and continuous improvement. By following these steps and embracing best practices, you can navigate the complex landscape of program development successfully. Remember, the journey may be challenging, but the rewards of creating innovative solutions are well worth it.

Author: Shreyansh Rane

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Program Development Cycle (PDLC): What You Need To Know

Vojin Deronjic

TABLE OF CONTENTS

What Is Program Development Cycle?

Problem definition, program design, structure charts, decision tables, coding the program, debugging and testing the program, documenting the program, deploying and maintaining the program, best practices of pdlc, keep your code simple, follow the dry principle, use version control.

Any good software and web development project starts with a plan. However, many software developers try to get straight into writing code. This is wrong because an adequately planned project reduces the number of possible mistakes and makes everything straightforward.

While it’s hard to imagine that a builder builds a building without a plan — why is it so easy for a software developer to start writing code? It shouldn’t be. A proper project should be planned according to the program development cycle.

In this post, we’ll guide you in detail through the program development cycle. We’ll explain what it is, why it’s important, cover each step, and share some best practices you can follow.

Program Development Cycle (PDLC) is a methodology that software engineers and product teams use to develop quality software. It provides a plan that breaks down program development into manageable stages and tasks. Each stage and task must be successfully completed before moving on to the next.

It may seem at first that PDLC is a sequential and linear process, but it actually requires a lot of interactions and iterations. It requires continual trial and error until you land on a program plan that works. Let’s take a look at each step of the PDLC.

The first step in the program development cycle is to define the problem. This is done by identifying and understanding why is the program or software developed. Usually, it’s in the form of a program specification. In larger development teams , the system analyst is responsible for this step.

The program specification defines the problem statement, what is required from the software development team, and what is the output of the solution to the problem they’ve been tasked with solving.

The next step in the process is to design the program itself. This process starts by focusing on the program's main goal, which the team defined in the previous step. The goal is then broken down into manageable parts, all of which play a different role in helping achieve the goal.

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The first step in designing a program is to identify the main routine of the program. Then, the main routine is divided into smaller parts called modules. A lead software developer should draw up a conceptual plan for each module to visualize how it will help in the overall process.

Depending on the module, people use different types of program design tools. The five major ones are algorithms, structure charts, flowcharts, decision tables, and pseudocode.

An algorithm is a step-by-step procedure used to solve a problem in the easiest way possible. An algorithm is an exact list of instructions and can be found in everyday life, not just in software engineering.

A structure chart looks at the top-down design of the program and breaks it into the smallest functional modules. It also describes each module’s function and sub-function in detail.

A flowchart is a diagram showing the logic of the program. It can also be considered a pictorial representation of an algorithm. Each step of the algorithm is represented in the form of various shapes of boxes, with the logical flow indicated by interconnecting arrows.

A decision table is a visual representation that specifies which actions to do based on given conditions. It is divided into four parts by two horizontal and two vertical lines. Software engineers and product teams use it to settle different combinations of inputs with their corresponding outputs.

It’s informative text or annotations written in plain English and used to represent an implementation of algorithms. Since it has no syntax, it can’t be compiled or interpreted by a computer.

Once the design program process is complete, the next step is to write the code in one or more programming languages, e.g., Java , .NET , Ruby , or whichever language works for the specific program.

Usually, coding itself is generally a small part of the entire PDLC. This step is completed once the entire program is codded and there are no syntax errors.

Even though one of the requirements to move onto this step is an error-free program, sometimes errors still occur. This is why the program is tested in this stage to find any logic, syntax, and other types of errors. The testing can be done internally, called alpha testing, or externally, called beta testing.

Once the errors have been found, the development team steps in to fix them.

After the testing is complete, the PDLC has reached its final stages. All the structure charts, flowcharts, decision tables, and pseudocode that were designed and used during the design phase are now part of the documentation that other people involved in the software project can use.

This phase is complete once the people working on it write a manual that includes an overview of the program, its functionalities, a beginner tutorial, explanations of significant features and all program commands, and a description of any error messages the program can generate.

The final step involves deploying the program for the customer. Usually, the development team keeps a tab on the project for some time, as this is usually the stage where actual usage problems can be seen.

This stage can last indefinitely, as the software needs to be maintained and upgraded regularly.

To ensure that the phases and steps we laid out in this piece are followed through correctly, you should follow a few best practices. They can be applied to some or all parts of the PDLC.

If you take one tip from this article, ensure it’s this one. A program with a simple code will help you stay coherent as you progress throughout the PDLC. On the other hand, a complex code can lead to a mess — one wrong input and you’ve created a domino effect.

Simple code is easy to test, it has logic boundaries on one domain, and others can easily understand it. In addition to staying coherent, simple code is more easily repurposed. Software development requires revisions and changes, and simple code will make those processes more manageable.

The goal of the DRY (Don’t Repeat Yourself) Principle is to help you reduce repetition and redundancies in your software engineering process. You can do this by grouping code into functions or replacing repetitions with abstractions.

DRY helps you create a single function that performs the instruction you want to once and then references this function each time you need it instead of doing it every time. If you want to change how the function works, you’ll only need to update it once.

Version control is a framework that makes it easier to track all code changes and syncs them with a master file that is stored on a remote server.

A version control system backs up your code, makes it easy for multiple developers to work on it, and streamlines the debugging process.

PDLC is an excellent methodology for building software. For it to be successful, the entire team needs to be on the same page. If you’re still unsure about the details of the program development cycle, the following questions and answers may clarify things and help you present the idea to your team.

Q1: Why do we need program development cycles?

The most significant benefit of PDLC is that it helps create quality software. While quality software can also be created using other methodologies, PDLC simplifies it.

The other most prominent benefit of PDLC is that it’s a structured way of developing software. It eliminates almost all complications in planning, even though it requires a lot of interactions and iterations.

Q2: What are the steps of program development?

1. Program development consists of the six following steps:

2. Problem definition: The first step, define why this software is being developed.

3. Program design: Design all the steps in the process using various program design tools.

4. Coding the program: Develop the software and ensure it’s error-free.

5. Debugging and testing: Fix all the errors and bugs that were missed in the previous step.

6. Documentation: Record in writing everything necessary for anyone using the developed software.

7. Deployment and maintenance: The final step, install the software and maintain it.

Q3: What is the problem definition in a program development life cycle?

In this phase of the PDLC, people define the problem they want to deal with in the program and decide on its boundaries. This stage aims to understand the problem statement, requirements, and output, according to tutorialspoint.com .

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What Is Problem Solving? How Software Engineers Approach Complex Challenges

From debugging an existing system to designing an entirely new software application, a day in the life of a software engineer is filled with various challenges and complexities. The one skill that glues these disparate tasks together and makes them manageable? Problem solving . 

Throughout this blog post, we’ll explore why problem-solving skills are so critical for software engineers, delve into the techniques they use to address complex challenges, and discuss how hiring managers can identify these skills during the hiring process. 

What Is Problem Solving?

But what exactly is problem solving in the context of software engineering? How does it work, and why is it so important?

Problem solving, in the simplest terms, is the process of identifying a problem, analyzing it, and finding the most effective solution to overcome it. For software engineers, this process is deeply embedded in their daily workflow. It could be something as simple as figuring out why a piece of code isn’t working as expected, or something as complex as designing the architecture for a new software system. 

In a world where technology is evolving at a blistering pace, the complexity and volume of problems that software engineers face are also growing. As such, the ability to tackle these issues head-on and find innovative solutions is not only a handy skill — it’s a necessity. 

The Importance of Problem-Solving Skills for Software Engineers

Problem-solving isn’t just another ability that software engineers pull out of their toolkits when they encounter a bug or a system failure. It’s a constant, ongoing process that’s intrinsic to every aspect of their work. Let’s break down why this skill is so critical.

Driving Development Forward

Without problem solving, software development would hit a standstill. Every new feature, every optimization, and every bug fix is a problem that needs solving. Whether it’s a performance issue that needs diagnosing or a user interface that needs improving, the capacity to tackle and solve these problems is what keeps the wheels of development turning.

It’s estimated that 60% of software development lifecycle costs are related to maintenance tasks, including debugging and problem solving. This highlights how pivotal this skill is to the everyday functioning and advancement of software systems.

Innovation and Optimization

The importance of problem solving isn’t confined to reactive scenarios; it also plays a major role in proactive, innovative initiatives . Software engineers often need to think outside the box to come up with creative solutions, whether it’s optimizing an algorithm to run faster or designing a new feature to meet customer needs. These are all forms of problem solving.

Consider the development of the modern smartphone. It wasn’t born out of a pre-existing issue but was a solution to a problem people didn’t realize they had — a device that combined communication, entertainment, and productivity into one handheld tool.

Increasing Efficiency and Productivity

Good problem-solving skills can save a lot of time and resources. Effective problem-solvers are adept at dissecting an issue to understand its root cause, thus reducing the time spent on trial and error. This efficiency means projects move faster, releases happen sooner, and businesses stay ahead of their competition.

Improving Software Quality

Problem solving also plays a significant role in enhancing the quality of the end product. By tackling the root causes of bugs and system failures, software engineers can deliver reliable, high-performing software. This is critical because, according to the Consortium for Information and Software Quality, poor quality software in the U.S. in 2022 cost at least $2.41 trillion in operational issues, wasted developer time, and other related problems.

Problem-Solving Techniques in Software Engineering

So how do software engineers go about tackling these complex challenges? Let’s explore some of the key problem-solving techniques, theories, and processes they commonly use.

Decomposition

Breaking down a problem into smaller, manageable parts is one of the first steps in the problem-solving process. It’s like dealing with a complicated puzzle. You don’t try to solve it all at once. Instead, you separate the pieces, group them based on similarities, and then start working on the smaller sets. This method allows software engineers to handle complex issues without being overwhelmed and makes it easier to identify where things might be going wrong.

Abstraction

In the realm of software engineering, abstraction means focusing on the necessary information only and ignoring irrelevant details. It is a way of simplifying complex systems to make them easier to understand and manage. For instance, a software engineer might ignore the details of how a database works to focus on the information it holds and how to retrieve or modify that information.

Algorithmic Thinking

At its core, software engineering is about creating algorithms — step-by-step procedures to solve a problem or accomplish a goal. Algorithmic thinking involves conceiving and expressing these procedures clearly and accurately and viewing every problem through an algorithmic lens. A well-designed algorithm not only solves the problem at hand but also does so efficiently, saving computational resources.

Parallel Thinking

Parallel thinking is a structured process where team members think in the same direction at the same time, allowing for more organized discussion and collaboration. It’s an approach popularized by Edward de Bono with the “ Six Thinking Hats ” technique, where each “hat” represents a different style of thinking.

In the context of software engineering, parallel thinking can be highly effective for problem solving. For instance, when dealing with a complex issue, the team can use the “White Hat” to focus solely on the data and facts about the problem, then the “Black Hat” to consider potential problems with a proposed solution, and so on. This structured approach can lead to more comprehensive analysis and more effective solutions, and it ensures that everyone’s perspectives are considered.

This is the process of identifying and fixing errors in code . Debugging involves carefully reviewing the code, reproducing and analyzing the error, and then making necessary modifications to rectify the problem. It’s a key part of maintaining and improving software quality.

Testing and Validation

Testing is an essential part of problem solving in software engineering. Engineers use a variety of tests to verify that their code works as expected and to uncover any potential issues. These range from unit tests that check individual components of the code to integration tests that ensure the pieces work well together. Validation, on the other hand, ensures that the solution not only works but also fulfills the intended requirements and objectives.

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Evaluating Problem-Solving Skills

We’ve examined the importance of problem-solving in the work of a software engineer and explored various techniques software engineers employ to approach complex challenges. Now, let’s delve into how hiring teams can identify and evaluate problem-solving skills during the hiring process.

Recognizing Problem-Solving Skills in Candidates

How can you tell if a candidate is a good problem solver? Look for these indicators:

• Previous Experience: A history of dealing with complex, challenging projects is often a good sign. Ask the candidate to discuss a difficult problem they faced in a previous role and how they solved it.
• Problem-Solving Questions: During interviews, pose hypothetical scenarios or present real problems your company has faced. Ask candidates to explain how they would tackle these issues. You’re not just looking for a correct solution but the thought process that led them there.
• Technical Tests: Coding challenges and other technical tests can provide insight into a candidate’s problem-solving abilities. Consider leveraging a platform for assessing these skills in a realistic, job-related context.

Assessing Problem-Solving Skills

Once you’ve identified potential problem solvers, here are a few ways you can assess their skills:

• Solution Effectiveness: Did the candidate solve the problem? How efficient and effective is their solution?
• Approach and Process: Go beyond whether or not they solved the problem and examine how they arrived at their solution. Did they break the problem down into manageable parts? Did they consider different perspectives and possibilities?
• Communication: A good problem solver can explain their thought process clearly. Can the candidate effectively communicate how they arrived at their solution and why they chose it?
• Adaptability: Problem-solving often involves a degree of trial and error. How does the candidate handle roadblocks? Do they adapt their approach based on new information or feedback?

Hiring managers play a crucial role in identifying and fostering problem-solving skills within their teams. By focusing on these abilities during the hiring process, companies can build teams that are more capable, innovative, and resilient.

Key Takeaways

As you can see, problem solving plays a pivotal role in software engineering. Far from being an occasional requirement, it is the lifeblood that drives development forward, catalyzes innovation, and delivers of quality software. 

By leveraging problem-solving techniques, software engineers employ a powerful suite of strategies to overcome complex challenges. But mastering these techniques isn’t simple feat. It requires a learning mindset, regular practice, collaboration, reflective thinking, resilience, and a commitment to staying updated with industry trends. 

For hiring managers and team leads, recognizing these skills and fostering a culture that values and nurtures problem solving is key. It’s this emphasis on problem solving that can differentiate an average team from a high-performing one and an ordinary product from an industry-leading one.

At the end of the day, software engineering is fundamentally about solving problems — problems that matter to businesses, to users, and to the wider society. And it’s the proficient problem solvers who stand at the forefront of this dynamic field, turning challenges into opportunities, and ideas into reality.

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Exploring the Problem Solving Cycle in Computer Science – Strategies, Techniques, and Tools

• Post author By bicycle-u
• Post date 08.12.2023

The world of computer science is built on the foundation of problem solving. Whether it’s finding a solution to a complex algorithm or analyzing data to make informed decisions, the problem solving cycle is at the core of every computer science endeavor.

At its essence, problem solving in computer science involves breaking down a complex problem into smaller, more manageable parts. This allows for a systematic approach to finding a solution by analyzing each part individually. The process typically starts with gathering and understanding the data or information related to the problem at hand.

Once the data is collected, computer scientists use various techniques and algorithms to analyze and explore possible solutions. This involves evaluating different approaches and considering factors such as efficiency, accuracy, and scalability. During this analysis phase, it is crucial to think critically and creatively to come up with innovative solutions.

After a thorough analysis, the next step in the problem solving cycle is designing and implementing a solution. This involves creating a detailed plan of action, selecting the appropriate tools and technologies, and writing the necessary code to bring the solution to life. Attention to detail and precision are key in this stage to ensure that the solution functions as intended.

The final step in the problem solving cycle is evaluating the solution and its effectiveness. This includes testing the solution against different scenarios and data sets to ensure its reliability and performance. If any issues or limitations are discovered, adjustments and optimizations are made to improve the solution.

In conclusion, the problem solving cycle is a fundamental process in computer science, involving analysis, data exploration, algorithm development, solution implementation, and evaluation. It is through this cycle that computer scientists are able to tackle complex problems and create innovative solutions that drive progress in the field of computer science.

Understanding the Importance

In computer science, problem solving is a crucial skill that is at the core of the problem solving cycle. The problem solving cycle is a systematic approach to analyzing and solving problems, involving various stages such as problem identification, analysis, algorithm design, implementation, and evaluation. Understanding the importance of this cycle is essential for any computer scientist or programmer.

Data Analysis and Algorithm Design

The first step in the problem solving cycle is problem identification, which involves recognizing and defining the issue at hand. Once the problem is identified, the next crucial step is data analysis. This involves gathering and examining relevant data to gain insights and understand the problem better. Data analysis helps in identifying patterns, trends, and potential solutions.

After data analysis, the next step is algorithm design. An algorithm is a step-by-step procedure or set of rules to solve a problem. Designing an efficient algorithm is crucial as it determines the effectiveness and efficiency of the solution. A well-designed algorithm takes into consideration the constraints, resources, and desired outcomes while implementing the solution.

Implementation and Evaluation

Once the algorithm is designed, the next step in the problem solving cycle is implementation. This involves translating the algorithm into a computer program using a programming language. The implementation phase requires coding skills and expertise in a specific programming language.

After implementation, the solution needs to be evaluated to ensure that it solves the problem effectively. Evaluation involves testing the program and verifying its correctness and efficiency. This step is critical to identify any errors or issues and to make necessary improvements or adjustments.

In conclusion, understanding the importance of the problem solving cycle in computer science is essential for any computer scientist or programmer. It provides a systematic and structured approach to analyze and solve problems, ensuring efficient and effective solutions. By following the problem solving cycle, computer scientists can develop robust algorithms, implement them in efficient programs, and evaluate their solutions to ensure their correctness and efficiency.

Identifying the Problem

In the problem solving cycle in computer science, the first step is to identify the problem that needs to be solved. This step is crucial because without a clear understanding of the problem, it is impossible to find a solution.

Identification of the problem involves a thorough analysis of the given data and understanding the goals of the task at hand. It requires careful examination of the problem statement and any constraints or limitations that may affect the solution.

During the identification phase, the problem is broken down into smaller, more manageable parts. This can involve breaking the problem down into sub-problems or identifying the different aspects or components that need to be addressed.

Identifying the problem also involves considering the resources and tools available for solving it. This may include considering the specific tools and programming languages that are best suited for the problem at hand.

By properly identifying the problem, computer scientists can ensure that they are focused on the right goals and are better equipped to find an effective and efficient solution. It sets the stage for the rest of the problem solving cycle, including the analysis, design, implementation, and evaluation phases.

Gathering the Necessary Data

Before finding a solution to a computer science problem, it is essential to gather the necessary data. Whether it’s writing a program or developing an algorithm, data serves as the backbone of any solution. Without proper data collection and analysis, the problem-solving process can become inefficient and ineffective.

The Importance of Data

In computer science, data is crucial for a variety of reasons. First and foremost, it provides the information needed to understand and define the problem at hand. By analyzing the available data, developers and programmers can gain insights into the nature of the problem and determine the most efficient approach for solving it.

Additionally, data allows for the evaluation of potential solutions. By collecting and organizing relevant data, it becomes possible to compare different algorithms or strategies and select the most suitable one. Data also helps in tracking progress and measuring the effectiveness of the chosen solution.

Data Gathering Process

The process of gathering data involves several steps. Firstly, it is necessary to identify the type of data needed for the particular problem. This may include numerical values, textual information, or other types of data. It is important to determine the sources of data and assess their reliability.

Once the required data has been identified, it needs to be collected. This can be done through various methods, such as surveys, experiments, observations, or by accessing existing data sets. The collected data should be properly organized, ensuring its accuracy and validity.

Data cleaning and preprocessing are vital steps in the data gathering process. This involves removing any irrelevant or erroneous data and transforming it into a suitable format for analysis. Properly cleaned and preprocessed data will help in generating reliable and meaningful insights.

Data Analysis and Interpretation

After gathering and preprocessing the data, the next step is data analysis and interpretation. This involves applying various statistical and analytical methods to uncover patterns, trends, and relationships within the data. By analyzing the data, programmers can gain valuable insights that can inform the development of an effective solution.

During the data analysis process, it is crucial to remain objective and unbiased. The analysis should be based on sound reasoning and logical thinking. It is also important to communicate the findings effectively, using visualizations or summaries to convey the information to stakeholders or fellow developers.

In conclusion, gathering the necessary data is a fundamental step in solving computer science problems. It provides the foundation for understanding the problem, evaluating potential solutions, and tracking progress. By following a systematic and rigorous approach to data gathering and analysis, developers can ensure that their solutions are efficient, effective, and well-informed.

Analyzing the Data

Once you have collected the necessary data, the next step in the problem-solving cycle is to analyze it. Data analysis is a crucial component of computer science, as it helps us understand the problem at hand and develop effective solutions.

To analyze the data, you need to break it down into manageable pieces and examine each piece closely. This process involves identifying patterns, trends, and outliers that may be present in the data. By doing so, you can gain insights into the problem and make informed decisions about the best course of action.

There are several techniques and tools available for data analysis in computer science. Some common methods include statistical analysis, data visualization, and machine learning algorithms. Each approach has its own strengths and limitations, so it’s essential to choose the most appropriate method for the problem you are solving.

Statistical Analysis

Statistical analysis involves using mathematical models and techniques to analyze data. It helps in identifying correlations, distributions, and other statistical properties of the data. By applying statistical tests, you can determine the significance and validity of your findings.

Data Visualization

Data visualization is the process of presenting data in a visual format, such as charts, graphs, or maps. It allows for a better understanding of complex data sets and facilitates the communication of findings. Through data visualization, patterns and trends can become more apparent, making it easier to derive meaningful insights.

Machine Learning Algorithms

Machine learning algorithms are powerful tools for analyzing large and complex data sets. These algorithms can automatically detect patterns and relationships in the data, leading to the development of predictive models and solutions. By training the algorithm on a labeled dataset, it can learn from the data and make accurate predictions or classifications.

In conclusion, analyzing the data is a critical step in the problem-solving cycle in computer science. It helps us gain a deeper understanding of the problem and develop effective solutions. Whether through statistical analysis, data visualization, or machine learning algorithms, data analysis plays a vital role in transforming raw data into actionable insights.

Exploring Possible Solutions

Once you have gathered data and completed the analysis, the next step in the problem-solving cycle is to explore possible solutions. This is where the true power of computer science comes into play. With the use of algorithms and the application of scientific principles, computer scientists can develop innovative solutions to complex problems.

During this stage, it is important to consider a variety of potential solutions. This involves brainstorming different ideas and considering their feasibility and potential effectiveness. It may be helpful to consult with colleagues or experts in the field to gather additional insights and perspectives.

Developing an Algorithm

One key aspect of exploring possible solutions is the development of an algorithm. An algorithm is a step-by-step set of instructions that outlines a specific process or procedure. In the context of problem solving in computer science, an algorithm provides a clear roadmap for implementing a solution.

The development of an algorithm requires careful thought and consideration. It is important to break down the problem into smaller, manageable steps and clearly define the inputs and outputs of each step. This allows for the creation of a logical and efficient solution.

Evaluating the Solutions

Once you have developed potential solutions and corresponding algorithms, the next step is to evaluate them. This involves analyzing each solution to determine its strengths, weaknesses, and potential impact. Consider factors such as efficiency, scalability, and resource requirements.

It may be helpful to conduct experiments or simulations to further assess the effectiveness of each solution. This can provide valuable insights and data to support the decision-making process.

Ultimately, the goal of exploring possible solutions is to find the most effective and efficient solution to the problem at hand. By leveraging the power of data, analysis, algorithms, and scientific principles, computer scientists can develop innovative solutions that drive progress and solve complex problems in the world of technology.

Evaluating the Options

Once you have identified potential solutions and algorithms for a problem, the next step in the problem-solving cycle in computer science is to evaluate the options. This evaluation process involves analyzing the potential solutions and algorithms based on various criteria to determine the best course of action.

Consider the Problem

Before evaluating the options, it is important to take a step back and consider the problem at hand. Understand the requirements, constraints, and desired outcomes of the problem. This analysis will help guide the evaluation process.

Analyze the Options

Next, it is crucial to analyze each solution or algorithm option individually. Look at factors such as efficiency, accuracy, ease of implementation, and scalability. Consider whether the solution or algorithm meets the specific requirements of the problem, and if it can be applied to related problems in the future.

Additionally, evaluate the potential risks and drawbacks associated with each option. Consider factors such as cost, time, and resources required for implementation. Assess any potential limitations or trade-offs that may impact the overall effectiveness of the solution or algorithm.

Select the Best Option

Based on the analysis, select the best option that aligns with the specific problem-solving goals. This may involve prioritizing certain criteria or making compromises based on the limitations identified during the evaluation process.

Remember that the best option may not always be the most technically complex or advanced solution. Consider the practicality and feasibility of implementation, as well as the potential impact on the overall system or project.

In conclusion, evaluating the options is a critical step in the problem-solving cycle in computer science. By carefully analyzing the potential solutions and algorithms, considering the problem requirements, and considering the limitations and trade-offs, you can select the best option to solve the problem at hand.

Making a Decision

Decision-making is a critical component in the problem-solving process in computer science. Once you have analyzed the problem, identified the relevant data, and generated a potential solution, it is important to evaluate your options and choose the best course of action.

Consider All Factors

When making a decision, it is important to consider all relevant factors. This includes evaluating the potential benefits and drawbacks of each option, as well as understanding any constraints or limitations that may impact your choice.

In computer science, this may involve analyzing the efficiency of different algorithms or considering the scalability of a proposed solution. It is important to take into account both the short-term and long-term impacts of your decision.

Weigh the Options

Once you have considered all the factors, it is important to weigh the options and determine the best approach. This may involve assigning weights or priorities to different factors based on their importance.

Using techniques such as decision matrices or cost-benefit analysis can help you systematically compare and evaluate different options. By quantifying and assessing the potential risks and rewards, you can make a more informed decision.

Remember: Decision-making in computer science is not purely subjective or based on personal preference. It is crucial to use analytical and logical thinking to select the most optimal solution.

In conclusion, making a decision is a crucial step in the problem-solving process in computer science. By considering all relevant factors and weighing the options using logical analysis, you can choose the best possible solution to a given problem.

Implementing the Solution

Once the problem has been analyzed and a solution has been proposed, the next step in the problem-solving cycle in computer science is implementing the solution. This involves turning the proposed solution into an actual computer program or algorithm that can solve the problem.

In order to implement the solution, computer science professionals need to have a strong understanding of various programming languages and data structures. They need to be able to write code that can manipulate and process data in order to solve the problem at hand.

During the implementation phase, the proposed solution is translated into a series of steps or instructions that a computer can understand and execute. This involves breaking down the problem into smaller sub-problems and designing algorithms to solve each sub-problem.

Computer scientists also need to consider the efficiency of their solution during the implementation phase. They need to ensure that the algorithm they design is able to handle large amounts of data and solve the problem in a reasonable amount of time. This often requires optimization techniques and careful consideration of the data structures used.

Once the code has been written and the algorithm has been implemented, it is important to test and debug the solution. This involves running test cases and checking the output to ensure that the program is working correctly. If any errors or bugs are found, they need to be fixed before the solution can be considered complete.

In conclusion, implementing the solution is a crucial step in the problem-solving cycle in computer science. It requires strong programming skills and a deep understanding of algorithms and data structures. By carefully designing and implementing the solution, computer scientists can solve problems efficiently and effectively.

Testing and Debugging

In computer science, testing and debugging are critical steps in the problem-solving cycle. Testing helps ensure that a program or algorithm is functioning correctly, while debugging analyzes and resolves any issues or bugs that may arise.

Testing involves running a program with specific input data to evaluate its output. This process helps verify that the program produces the expected results and handles different scenarios correctly. It is important to test both the normal and edge cases to ensure the program’s reliability.

Debugging is the process of identifying and fixing errors or bugs in a program. When a program does not produce the expected results or crashes, it is necessary to go through the code to find and fix the problem. This can involve analyzing the program’s logic, checking for syntax errors, and using debugging tools to trace the flow of data and identify the source of the issue.

Data analysis plays a crucial role in both testing and debugging. It helps to identify patterns, anomalies, or inconsistencies in the program’s behavior. By analyzing the data, developers can gain insights into potential issues and make informed decisions on how to improve the program’s performance.

In conclusion, testing and debugging are integral parts of the problem-solving cycle in computer science. Through testing and data analysis, developers can verify the correctness of their programs and identify and resolve any issues that may arise. This ensures that the algorithms and programs developed in computer science are robust, reliable, and efficient.

Iterating for Improvement

In computer science, problem solving often involves iterating through multiple cycles of analysis, solution development, and evaluation. This iterative process allows for continuous improvement in finding the most effective solution to a given problem.

The problem solving cycle starts with problem analysis, where the specific problem is identified and its requirements are understood. This step involves examining the problem from various angles and gathering all relevant information.

Once the problem is properly understood, the next step is to develop an algorithm or a step-by-step plan to solve the problem. This algorithm is a set of instructions that, when followed correctly, will lead to the solution.

After the algorithm is developed, it is implemented in a computer program. This step involves translating the algorithm into a programming language that a computer can understand and execute.

Once the program is implemented, it is then tested and evaluated to ensure that it produces the correct solution. This evaluation step is crucial in identifying any errors or inefficiencies in the program and allows for further improvement.

If any issues or problems are found during testing, the cycle iterates, starting from problem analysis again. This iterative process allows for refinement and improvement of the solution until the desired results are achieved.

Iterating for improvement is a fundamental concept in computer science problem solving. By continually analyzing, developing, and evaluating solutions, computer scientists are able to find the most optimal and efficient approaches to solving problems.

Documenting the Process

Documenting the problem-solving process in computer science is an essential step to ensure that the cycle is repeated successfully. The process involves gathering information, analyzing the problem, and designing a solution.

During the analysis phase, it is crucial to identify the specific problem at hand and break it down into smaller components. This allows for a more targeted approach to finding the solution. Additionally, analyzing the data involved in the problem can provide valuable insights and help in designing an effective solution.

Once the analysis is complete, it is important to document the findings. This documentation can take various forms, such as written reports, diagrams, or even code comments. The goal is to create a record that captures the problem, the analysis, and the proposed solution.

Documenting the process serves several purposes. Firstly, it allows for easy communication and collaboration between team members or future developers. By documenting the problem, analysis, and solution, others can easily understand the thought process behind the solution and potentially build upon it.

Secondly, documenting the process provides an opportunity for reflection and improvement. By reviewing the documentation, developers can identify areas where the problem-solving cycle can be strengthened or optimized. This continuous improvement is crucial in the field of computer science, as new challenges and technologies emerge rapidly.

In conclusion, documenting the problem-solving process is an integral part of the computer science cycle. It allows for effective communication, collaboration, and reflection on the solutions devised. By taking the time to document the process, developers can ensure a more efficient and successful problem-solving experience.

Communicating the Solution

Once the problem solving cycle is complete, it is important to effectively communicate the solution. This involves explaining the analysis, data, and steps taken to arrive at the solution.

Analyzing the Problem

During the problem solving cycle, a thorough analysis of the problem is conducted. This includes understanding the problem statement, gathering relevant data, and identifying any constraints or limitations. It is important to clearly communicate this analysis to ensure that others understand the problem at hand.

Presenting the Solution

The next step in communicating the solution is presenting the actual solution. This should include a detailed explanation of the steps taken to solve the problem, as well as any algorithms or data structures used. It is important to provide clear and concise descriptions of the solution, so that others can understand and reproduce the results.

Overall, effective communication of the solution in computer science is essential to ensure that others can understand and replicate the problem solving process. By clearly explaining the analysis, data, and steps taken, the solution can be communicated in a way that promotes understanding and collaboration within the field of computer science.

Reflecting and Learning

Reflecting and learning are crucial steps in the problem solving cycle in computer science. Once a problem has been solved, it is essential to reflect on the entire process and learn from the experience. This allows for continuous improvement and growth in the field of computer science.

During the reflecting phase, one must analyze and evaluate the problem solving process. This involves reviewing the initial problem statement, understanding the constraints and requirements, and assessing the effectiveness of the chosen algorithm and solution. It is important to consider the efficiency and accuracy of the solution, as well as any potential limitations or areas for optimization.

By reflecting on the problem solving cycle, computer scientists can gain valuable insights into their own strengths and weaknesses. They can identify areas where they excelled and areas where improvement is needed. This self-analysis helps in honing problem solving skills and becoming a better problem solver.

Learning from Mistakes

Mistakes are an integral part of the problem solving cycle, and they provide valuable learning opportunities. When a problem is not successfully solved, it is essential to analyze the reasons behind the failure and learn from them. This involves identifying errors in the algorithm or solution, understanding the underlying concepts or principles that were misunderstood, and finding alternative approaches or strategies.

Failure should not be seen as a setback, but rather as an opportunity for growth. By learning from mistakes, computer scientists can improve their problem solving abilities and expand their knowledge and understanding of computer science. It is through these failures and the subsequent learning process that new ideas and innovations are often born.

Continuous Improvement

Reflecting and learning should not be limited to individual problem solving experiences, but should be an ongoing practice. As computer science is a rapidly evolving field, it is crucial to stay updated with new technologies, algorithms, and problem solving techniques. Continuous learning and improvement contribute to staying competitive and relevant in the field.

Computer scientists can engage in continuous improvement by seeking feedback from peers, participating in research and development activities, attending conferences and workshops, and actively seeking new challenges and problem solving opportunities. This dedication to learning and improvement ensures that one’s problem solving skills remain sharp and effective.

In conclusion, reflecting and learning are integral parts of the problem solving cycle in computer science. They enable computer scientists to refine their problem solving abilities, learn from mistakes, and continuously improve their skills and knowledge. By embracing these steps, computer scientists can stay at the forefront of the ever-changing world of computer science and contribute to its advancements.

Applying Problem Solving in Real Life

In computer science, problem solving is not limited to the realm of programming and algorithms. It is a skill that can be applied to various aspects of our daily lives, helping us to solve problems efficiently and effectively. By using the problem-solving cycle and applying the principles of analysis, data, solution, algorithm, and cycle, we can tackle real-life challenges with confidence and success.

The first step in problem-solving is to analyze the problem at hand. This involves breaking it down into smaller, more manageable parts and identifying the key issues or goals. By understanding the problem thoroughly, we can gain insights into its root causes and potential solutions.

For example, let’s say you’re facing a recurring issue in your daily commute – traffic congestion. By analyzing the problem, you may discover that the main causes are a lack of alternative routes and a lack of communication between drivers. This analysis helps you identify potential solutions such as using navigation apps to find alternate routes or promoting carpooling to reduce the number of vehicles on the road.

Gathering and Analyzing Data

Once we have identified the problem, it is important to gather relevant data to support our analysis. This may involve conducting surveys, collecting statistics, or reviewing existing research. By gathering data, we can make informed decisions and prioritize potential solutions based on their impact and feasibility.

Continuing with the traffic congestion example, you may gather data on the average commute time, the number of vehicles on the road, and the impact of carpooling on congestion levels. This data can help you analyze the problem more accurately and determine the most effective solutions.

Generating and Evaluating Solutions

After analyzing the problem and gathering data, the next step is to generate potential solutions. This can be done through brainstorming, researching best practices, or seeking input from experts. It is important to consider multiple options and think outside the box to find innovative and effective solutions.

For our traffic congestion problem, potential solutions can include implementing a smart traffic management system that optimizes traffic flow or investing in public transportation to incentivize people to leave their cars at home. By evaluating each solution’s potential impact, cost, and feasibility, you can make an informed decision on the best course of action.

Implementing and Iterating

Once a solution has been chosen, it is time to implement it in real life. This may involve developing a plan, allocating resources, and executing the solution. It is important to monitor the progress and collect feedback to learn from the implementation and make necessary adjustments.

For example, if the chosen solution to address traffic congestion is implementing a smart traffic management system, you would work with engineers and transportation authorities to develop and deploy the system. Regular evaluation and iteration of the system’s performance would ensure that it is effective and making a positive impact on reducing congestion.

By applying the problem-solving cycle derived from computer science to real-life situations, we can approach challenges with a systematic and analytical mindset. This can help us make better decisions, improve our problem-solving skills, and ultimately achieve more efficient and effective solutions.

Building Problem Solving Skills

In the field of computer science, problem-solving is a fundamental skill that is crucial for success. Whether you are a computer scientist, programmer, or student, developing strong problem-solving skills will greatly benefit your work and studies. It allows you to approach challenges with a logical and systematic approach, leading to efficient and effective problem resolution.

The Problem Solving Cycle

Problem-solving in computer science involves a cyclical process known as the problem-solving cycle. This cycle consists of several stages, including problem identification, data analysis, solution development, implementation, and evaluation. By following this cycle, computer scientists are able to tackle complex problems and arrive at optimal solutions.

Importance of Data Analysis

Data analysis is a critical step in the problem-solving cycle. It involves gathering and examining relevant data to gain insights and identify patterns that can inform the development of a solution. Without proper data analysis, computer scientists may overlook important information or make unfounded assumptions, leading to subpar solutions.

To effectively analyze data, computer scientists can employ various techniques such as data visualization, statistical analysis, and machine learning algorithms. These tools enable them to extract meaningful information from large datasets and make informed decisions during the problem-solving process.

Developing Effective Solutions

Developing effective solutions requires creativity, critical thinking, and logical reasoning. Computer scientists must evaluate multiple approaches, consider various factors, and assess the feasibility of different solutions. They should also consider potential limitations and trade-offs to ensure that the chosen solution addresses the problem effectively.

Furthermore, collaboration and communication skills are vital when building problem-solving skills. Computer scientists often work in teams and need to effectively communicate their ideas, propose solutions, and address any challenges that arise during the problem-solving process. Strong interpersonal skills facilitate collaboration and enhance problem-solving outcomes.

• Mastering programming languages and algorithms
• Staying updated with technological advancements in the field
• Practicing problem solving through coding challenges and projects
• Seeking feedback and learning from mistakes
• Continuing to learn and improve problem-solving skills

By following these strategies, individuals can strengthen their problem-solving abilities and become more effective computer scientists or programmers. Problem-solving is an essential skill in computer science and plays a central role in driving innovation and advancing the field.

Questions and answers:

What is the problem solving cycle in computer science.

The problem solving cycle in computer science refers to a systematic approach that programmers use to solve problems. It involves several steps, including problem definition, algorithm design, implementation, testing, and debugging.

How important is the problem solving cycle in computer science?

The problem solving cycle is extremely important in computer science as it allows programmers to effectively tackle complex problems and develop efficient solutions. It helps in organizing the thought process and ensures that the problem is approached in a logical and systematic manner.

What are the steps involved in the problem solving cycle?

The problem solving cycle typically consists of the following steps: problem definition and analysis, algorithm design, implementation, testing, and debugging. These steps are repeated as necessary until a satisfactory solution is achieved.

Can you explain the problem definition and analysis step in the problem solving cycle?

During the problem definition and analysis step, the programmer identifies and thoroughly understands the problem that needs to be solved. This involves analyzing the requirements, constraints, and possible inputs and outputs. It is important to have a clear understanding of the problem before proceeding to the next steps.

Why is testing and debugging an important step in the problem solving cycle?

Testing and debugging are important steps in the problem solving cycle because they ensure that the implemented solution functions as intended and is free from errors. Through testing, the programmer can identify and fix any issues or bugs in the code, thereby improving the quality and reliability of the solution.

What is the problem-solving cycle in computer science?

The problem-solving cycle in computer science refers to the systematic approach that computer scientists use to solve problems. It involves various steps, including problem analysis, algorithm design, coding, testing, and debugging.

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DEV Community

Posted on Sep 24, 2022

15 Problem Solving Steps to Improve Your Development Skills

Introduction.

I will tell you everything I have learned about problem-solving during my more than ten years of work experience in software engineering.

This guide follows the universal problem-solving framework. That is why this can also be helpful for people who aren't software engineers.

Here is a shortened list of points of the problem-solving framework.

Define the problem

Generate potential solutions and choose one solution.

• Implement and follow up

Let's dive into the points and implement the framework for the software engineering domain.

This is the most important step. If we do not define the problem correctly, we won't be able to solve it. Often, we don't formulate the problem correctly from the outset, or we don't have enough data to know what the real problem is. Not having a well-defined problem from the beginning is an issue, but, as we will see in this article, some of our steps will help us to do it.

Know that your setup is unique

Maybe you are using an old version of a software or library.

The combination of different hardware, operating systems, software, and different versions of the above will make each setup unique.

The uniqueness of each setup is one of the reasons that many software vendors ask you to send your system information when you create a bug report or report an issue on an open-source project.

When defining the problem, you need to consider the specifics of your system.

Reduce the scope of the issue

You need to isolate the problem. This way, you can find the root cause of the problem.

If you have a tricky issue and you think that your system contributes to the problem, try to reproduce the issue on:

Isolated environment

Any software can be run in an isolated environment. Here are some examples.

If you are working on a desktop application, you can run the setup in a docker container.

If you're doing system development, you can boot the operating system in safe mode.

If you're doing browser extension development, you can disable other browser extensions.

In general, if you're working with a source code, you can isolate a feature logic or component from the rest of the system.

The previous version of the software

You might want to use previous (stable) versions of the software and see if the issue reproduces.

The art of search

To ask the right question is already half the solution to a problem. – Carl Gustav Jung

You have reduced the scope of the issue and have a great candidate for the root cause. Great! Now is the time to find the solution to the problem.

One of the fastest ways to find solutions to software problems is a Google search.

Sometimes, we find the solutions pretty quickly. But, if the issue is not common, we need some more work.

Here we need some searching skills.

First of all, we need to find the language needed to describe the issue. Many times Google will give hints on the language used by more advanced users.

Let's consider a trivial example. Imagine that we are learning frontend development, and we want to design our new website in a way that will look beautiful on all screen sizes and will be easy to use on all devices. We will go to Google and type something like this: "change website structure based on screen sizes"

Quickly looking onto the search results page, specifically onto the search results page's titles and the "Related searches" section, we can find that the type of websites we are looking for is called responsive websites.

So if our initial query didn't have the industry term, we can use the term "responsive design" in the subsequent queries.

This is the industry jargon. Using industry jargon in your search queries is important. Many articles, videos, and posts on the Q&A websites use industry jargon. You will certainly find great resources when you use industry jargon in your searches.

Our initial query: "change website structure based on screen sizes" , returned a lot of beginner-friendly articles. Some of these articles didn't use jargon in their titles. You might think it is okay to use simple words and avoid jargon, but that's not the best idea. Industry jargon will help you find solutions to non-trivial issues because they are written for professionals and advanced-level users.

We've seen a nice feature of the Google search. Being a total newbie in the problem area, we discovered terms used by professionals. Similarly, we can further improve our query step by step.

If we didn't find the answer with a simple search, we could use Google Search operators.

One of the most-used operators that will help us during the search is the text in double quotes like this: "problem solving in software engineering" .

This will tell Google to fetch the pages only if they have the "problem solving in software engineering" phrase exactly as it is written (exact match). Variations and partial queries like "problem solving" will not be considered. For example, exact match search is helpful if you want to search with the exact error message.

Another helpful operator is the website name. If you want to search only inside stackoverflow.com website, you can add site:stackoverflow.com before or after the search query. For example:

You might think you can do the same if you search for the answer on the StackOverflow website.

You can do this, but keep in mind that this is a completely different search engine. This is Stackoverflow's internal search engine.

In general, Google is superior to internal search engines because of the PageRank and RankBrain algorithms and because, obviously, Google possesses more data and advanced search algorithms.

Keep in mind that some articles might not be available on Google's search engine. For example, a search on StackOverflow might provide results you might not find in Google's search results. This can happen if an article is new and Google hasn't yet added it to its index.

If I have lost you there, don't worry. If you check how Google search works , you will improve your search game.

Now that we have defined the problem, it's time to generate potential solutions.

We did the steps before and eventually found the root cause of the problem. The initial root cause can turn out not to be the actual one. We might find that out if we start applying the potential solutions.

In practice, potential-solution generation consists of two phases.

The rapid phase: We search for the solution on the internet, and we apply the suggested solutions. In a nutshell, we check if the problem has occurred to other people. Many issues are common, and the solution is already provided on the internet. We don't want to reinvent the wheel or stay longer with the problem, so it's wise to look for a solution on the internet as the first step.

The strategic phase: We accept that the problem is not simple and we give ourselves time to solve it. We take the problem apart and do deeper research.

Here we formulate hypotheses, isolate them, check the isolated hypotheses, and prove them wrong or right. We can start fixing the problem as soon we have found the reason.

Apply fixes

Let's talk about the rapid solution-seeking process.

If we have a coding problem, it's not a secret that many of the answers will come from the Stackoverflow website. If there is an accepted answer, chances are high that the proposed solution will work.

Note that the process is similar for all other helpful Q&A websites, even in different industries and domains. Most of them have a notion of an accepted/recommended solution and upvoted answers.

If the accepted answer doesn't work, we tend to try out the other solutions that have been given positive usefulness points (upvotes).

If popular Q&A websites do not have the answers we are looking for, we can check the other pages in the search results. In many cases, there are great blog posts addressing the issue with detailed explanations, sometimes going into the fundamentals. They may help us to find the solution.

Remember one thing, though. Everything on the internet does not come with the highest quality. If an article for the solution to your problem ranks on the first page of Google, that does not necessarily mean that the article is the best among the peers and that the solution will work.

Did you even read the docs?

So, the answer wasn't on the community forum websites or even in the blogs. That's bad. That means you have a tricky problem. Either it is a weird case, or the software or library you are using is not that popular. Now, it is time to start the deep research phase.

It may be a good idea to read the docs now. I would start with the topics around the issue. If that is not enough and you have enough time, you can read more and more about the library or software. Note that the information in the docs should be up to date. If it turns out that that's not the case, then you can report an issue.

Ask your colleagues

This is a crucial step. Whether your colleagues will be able to give you an answer depends on the question. The problem that you are facing today may be a well-known problem for your colleagues. Your team might forget to tell you about well-known problems or shortcomings of the system. Even if it's not a well-known problem, you might have a colleague who has deeper knowledge in the domain of the issue and can help you find the fix quickly.

You need to be shameless here. Not asking questions can be counterproductive. If your colleagues can help you solve the problem in a couple of minutes, but you spend more time figuring it out yourself without asking the question, then you're wasting time. That means you are not working efficiently.

Ask the community

If the rapid phase didn't help us to find the solution, we must give ourselves time and change tactics. This and the following steps show how we can do it.

We visited community Q&A websites in the previous steps. If nobody asked a question about the problem we are facing on the Q&A websites like Stackoverflow and the community and support forums, we can do it ourselves and ask the question.

Here's a way to maximize the chances of getting answers in the shortest possible time. Official vendor websites point out recommended places for community support. Many times you can find them in the "Contact us" section. There, you can find vendors' recommended Q/A websites for asking questions.

When a company indicates recommended places to ask questions, that usually means that their employees or the most active community members are regularly checking the questions there, which means there's a great chance to get quick and reliable answers to your questions.

You can see the React.js framework's "Where To Get Support" page below.

In any case, it won't hurt to ask questions on generic Q&A websites stackoverflow.com or on another related website of the Stack Exchange network in addition to the vendor forums.

Reading the documents as was stated in the previous steps would help us formulate the question in a way that would be easy for the community to answer it.

Additionally, Stackoverflow and other websites suggest including minimal code snippets and environment details so that people can quickly reproduce the issue and check their answers before posting them. This means that you would help people to help you! The isolated hypothesis checking process discussed above might help us to generate a minimal isolated snippet and attach it to the question.

You did the previous steps as fast as possible, and you posted the question on the community forums. You probably never took a break while doing all the steps discussed above. You may have been in such a rush that you didn't even notice how time passed.

But the whole world is not that interested in the problem you are solving! Community members are living their lives and aren't impatiently waiting for questions. Though, in some cases, you might be lucky and get a fast answer.

Now that you posted the question in the community forum, it's time to take a break!

The break would help in the following ways:

You can have coffee or refreshments, take a walk, or speak with colleagues. You can do other non-work-related stuff planned for the day, etcetera.

Sometimes you need to slow down to understand the problem. As surprising as this might sound, you might not have thought enough about the problem while doing the previous steps.

I remember a day when a colleague of mine was honest and said he had tried to solve a problem without thinking about it. He then realized that the problem was not trivial and that he needed to understand it to solve it. The reason he "confessed" was he wanted to tell us that the problem was tricky and that we needed to allocate a lot more time for it and avoid entering autopilot mode.

Locate the source code

The order of the steps can be different for different people. For example, some people love to check the source code even before they check the documentation.

The source code provides the most accurate documentation.

Documentation can be incorrect or obsolete, but the source code cannot lie.

Note that you need to make sure you are checking the source code of the correct version of the software.

Obviously, the source code needs to be readily available.

Fortunately, the majority of popular software development tools are open source. That means that you can easily find their source code on the internet.

If the software is not open source, it's still possible to analyze its source code. Here's how.

If the software is on your machine and is written in scripting and interpreting languages like Python or Bash, then you already have the source on your machine. Easy.

If it's written in Java or another bytecode language, you can use bytecode decompilers to recover the code from the bytecode.

Here's what you can do if the software is hosted on the cloud or it's a software as a service (SaaS).

If the problem occurs on the frontend, you have the code because Javascript is a scripting language. Your browser downloads the Javascript source code in order to execute it.

The easiest way to analyze what is happening with the Javascript code is by using a browser debugger.

During the frontend debug, some requests would go to the backend with APIs. If you see the wrong answer coming from the API (backend), you can check which endpoint it is. When inspecting the Javascript in your browser, you will have the endpoint and a request payload. You can tweak and repeat the request and investigate the endpoint.

The backend behind the API, unfortunately, is a black box. You can't see the code that runs the backend behind the API. However, sometimes, you can get an idea of what's happening there.

Now that you have looked at the source code, you can debug the code in your mind. Many experienced software engineers can debug and run many lines of code in their minds.

You can use debuggers to debug it if the issue is in the software code you are working on.

If the issue happens on the library code, debugging is harder. It will take quite some time to set up a debugger on the library's source code. In most cases, this is unnecessary because you are not one of the library developers.

Overlooked support channels

This step can be skipped by many of us. Your company might have dedicated support channels with software and library vendors. If this is the case, you already guessed it: Ping them.

When we were hosting a non-trivial setup on Amazon Web Services (AWS), sometimes we needed to go deep into the documentation to check how to configure some tasks.

We were spending considerable time on this until a colleague checked and found that our company had dedicated support from Amazon. This is not common, and it makes sense for big accounts.

Instead of spending time figuring out how to do the next advanced config, our colleague would fire an email to the support center.

The colleague didn't stop there. Turned out that there was phone support as well. Email support usually takes longer than phone support, right? So instead of sending emails, our colleague would immediately pick up the phone and call them.

I think the support team at Amazon Web Services hated him, but we loved him!

It is important to note that people working in support centers must know the documentation very well, and the chances are higher that they will give you great advice in a much shorter amount of time.

Moreover, support teams have access to powerful tools that are not meant to be shared with clients. Remember — with great power comes great responsibility. That's one of the reasons that companies don't give the public access to these sensitive tools.

For example, the Amazon Web Services support team sometimes did the configuration for us instead of telling us how to do it. They even made changes that wouldn't have been possible with the public user interface.

Call it a day

So, you've spent a lot of time on the issue, and it still isn't resolved? Well, there's a "weapon" in this case: Call it a day.

If it is time to go home, you can go home. Staying overtime to solve the problem during the day may be counterproductive for several reasons:

You have now stayed too long with the problem. You have some crystalized ideas about how the problem should be solved. It's difficult to come up with different approaches if you don't take a break.

Secondly, you might be tired. It is the end of the day, and you have spent much more energy solving a difficult problem than you would spend on a regular day. With a tired brain and body, you can't perform efficiently.

You will need full energy tomorrow, so you need a good rest. If you get high-quality sleep, you can even go to work earlier than usual. You will not be disturbed much when you work early in the morning, and you will have a mind.

So it's a good idea to move the overtime hours to the next morning. As a bonus, since you will come earlier than usual the next morning, you can leave sooner that day.

The solution, or even several possible solutions, may come to you while you're resting during the night. As you might know, this happens.

I listed some reasons you need to call it a day.

What if it's not the time to go home?

If it's around 4 p.m., you might also want to call it a day and work on a routine task that you have to do anyway.

Choosing a solution

No matter what process we use when generating solutions, we will have one or many solutions. We need to implement the solution that is the most efficient, maintainable, and doesn't change our existing system too much.

Sometimes, the decision is evident, and we can implement the solution.

Other times, the solution might require significant or risky changes. If that is the case, it's a good idea to talk with the team and together decide which path to take to solve the problem.

Implement and follow-up

I've already talked about defining the problem, seeking the solution, and choosing the best one. The listed items are a critical part of problem-solving. The way a solution is implemented, however, is crucial as well.

In an ideal case, the implementation would be reliable, fundamental, fitting in the design flow, and clean.

There are situations when the best solution cannot be implemented because of shortcomings in our setup. In this case, we need to adjust the solution to the system or implement a suboptimal solution. Remember that something that works today is better than something perfect that would work sometime in the future.

Note that sometimes we might be unable to apply the best solution, not because of our system's shortcomings but because third-party software and services are the blockers.

Viola: You have solved the problem by finding the root cause, evaluating the solutions, picking one of them, and implementing it in our system.

What's next?

We need to revert the fixes that didn't work.

When we stay longer with the issue, we tend to apply many changes, even the ones that do not affect the system in any way or worse than that, the changes may have a side effect on other system functions. We need to clean up the code that didn't help. Though, it will help a lot when the cleanup is done during the evaluation process - when the solutions are tried.

Delivering the solution

After the cleanup, we can do the delivery process. We need to After the cleanup, we can start working on the delivery process. We must ensure that we have thoroughly tested the change and that the code follows the accepted standards.

If everything is in place, we can send the code to be reviewed so that team members can evaluate the final solution.

If everything is fine, the solution can be delivered to end users. Since the delivery process is different depending on the type of software, I will not go into the details.

You just have solved a problem. You might have learned important lessons. You can document what you've learned because writing notes about the solution will crystalize the solution for you. Since you might forget the solution after a year, noting down the solution will help you if the problem occurs anywhere else.

I have a great advice for you that I have learned from one of my colleagues. It's a good idea to start doing this if you aren't doing it already.

One day we were pair programming with a senior colleague of mine. We were sitting in front of his computer. I saw a file that day that would change the way I work.

What was inside this magic file?

It was a simple text file containing server URLs, usernames, workarounds, fixes to common problems, and everything you might need to look up regularly. I could not believe my eyes. How could I have been in the industry for five years and have not come up with such a brilliant idea?

Quickly after, I created my version of the file.

I use this kind of file for each company and project. If you want to create the file, I suggest making it an offline plain text file. The file should be as simple as possible and, ideally, accessible with just one click.

You may want to add the solution to the problem you just solved to the file.

In Addition, if you posted a question in the Q&A forums, it would be great if you would provide the answer that worked for you. You would help the community.

Software engineers solve problems every day. Many of the issues that we see are new. This is because:

Software changes fast. With version updates, a piece of software can change drastically.

Software and libraries come and go, and a different set of software combined with different project requirements often creates unseen-before problems.

If we notice patterns in these problems and use a well-defined process to solve them, we will become great problem solvers in software engineering.

This guide introduced a framework for problem-solving and an implementation of the framework, with detailed sequential steps. Hope that you liked it!

You can find my publications on my LinkedIn profile page.

Please feel free to add additional steps you use when solving problems in the comments below.

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Problem Solving

Foundations course, introduction.

Before we start digging into some pretty nifty JavaScript, we need to begin talking about problem solving : the most important skill a developer needs.

Problem solving is the core thing software developers do. The programming languages and tools they use are secondary to this fundamental skill.

From his book, “Think Like a Programmer” , V. Anton Spraul defines problem solving in programming as:

Problem solving is writing an original program that performs a particular set of tasks and meets all stated constraints.

The set of tasks can range from solving small coding exercises all the way up to building a social network site like Facebook or a search engine like Google. Each problem has its own set of constraints, for example, high performance and scalability may not matter too much in a coding exercise but it will be vital in apps like Google that need to service billions of search queries each day.

New programmers often find problem solving the hardest skill to build. It’s not uncommon for budding programmers to breeze through learning syntax and programming concepts, yet when trying to code something on their own, they find themselves staring blankly at their text editor not knowing where to start.

The best way to improve your problem solving ability is by building experience by making lots and lots of programs. The more practice you have the better you’ll be prepared to solve real world problems.

In this lesson we will walk through a few techniques that can be used to help with the problem solving process.

Lesson overview

This section contains a general overview of topics that you will learn in this lesson.

• Explain the three steps in the problem solving process.
• Explain what pseudocode is and be able to use it to solve problems.
• Be able to break a problem down into subproblems.

Understand the problem

The first step to solving a problem is understanding exactly what the problem is. If you don’t understand the problem, you won’t know when you’ve successfully solved it and may waste a lot of time on a wrong solution .

To gain clarity and understanding of the problem, write it down on paper, reword it in plain English until it makes sense to you, and draw diagrams if that helps. When you can explain the problem to someone else in plain English, you understand it.

Now that you know what you’re aiming to solve, don’t jump into coding just yet. It’s time to plan out how you’re going to solve it first. Some of the questions you should answer at this stage of the process:

• Does your program have a user interface? What will it look like? What functionality will the interface have? Sketch this out on paper.
• What inputs will your program have? Will the user enter data or will you get input from somewhere else?
• What’s the desired output?
• Given your inputs, what are the steps necessary to return the desired output?

The last question is where you will write out an algorithm to solve the problem. You can think of an algorithm as a recipe for solving a particular problem. It defines the steps that need to be taken by the computer to solve a problem in pseudocode.

Pseudocode is writing out the logic for your program in natural language instead of code. It helps you slow down and think through the steps your program will have to go through to solve the problem.

Here’s an example of what the pseudocode for a program that prints all numbers up to an inputted number might look like:

This is a basic program to demonstrate how pseudocode looks. There will be more examples of pseudocode included in the assignments.

Divide and conquer

From your planning, you should have identified some subproblems of the big problem you’re solving. Each of the steps in the algorithm we wrote out in the last section are subproblems. Pick the smallest or simplest one and start there with coding.

It’s important to remember that you might not know all the steps that you might need up front, so your algorithm may be incomplete -— this is fine. Getting started with and solving one of the subproblems you have identified in the planning stage often reveals the next subproblem you can work on. Or, if you already know the next subproblem, it’s often simpler with the first subproblem solved.

Many beginners try to solve the big problem in one go. Don’t do this . If the problem is sufficiently complex, you’ll get yourself tied in knots and make life a lot harder for yourself. Decomposing problems into smaller and easier to solve subproblems is a much better approach. Decomposition is the main way to deal with complexity, making problems easier and more approachable to solve and understand.

In short, break the big problem down and solve each of the smaller problems until you’ve solved the big problem.

Solving Fizz Buzz

To demonstrate this workflow in action, let’s solve a common programming exercise: Fizz Buzz, explained in this wiki article .

Understanding the problem

Write a program that takes a user’s input and prints the numbers from one to the number the user entered. However, for multiples of three print Fizz instead of the number and for the multiples of five print Buzz . For numbers which are multiples of both three and five print FizzBuzz .

This is the big picture problem we will be solving. But we can always make it clearer by rewording it.

Write a program that allows the user to enter a number, print each number between one and the number the user entered, but for numbers that divide by 3 without a remainder print Fizz instead. For numbers that divide by 5 without a remainder print Buzz and finally for numbers that divide by both 3 and 5 without a remainder print FizzBuzz .

Does your program have an interface? What will it look like? Our FizzBuzz solution will be a browser console program, so we don’t need an interface. The only user interaction will be allowing users to enter a number.

What inputs will your program have? Will the user enter data or will you get input from somewhere else? The user will enter a number from a prompt (popup box).

What’s the desired output? The desired output is a list of numbers from 1 to the number the user entered. But each number that is divisible by 3 will output Fizz , each number that is divisible by 5 will output Buzz and each number that is divisible by both 3 and 5 will output FizzBuzz .

Writing the pseudocode

What are the steps necessary to return the desired output? Here is an algorithm in pseudocode for this problem:

Dividing and conquering

As we can see from the algorithm we developed, the first subproblem we can solve is getting input from the user. So let’s start there and verify it works by printing the entered number.

With JavaScript, we’ll use the “prompt” method.

The above code should create a little popup box that asks the user for a number. The input we get back will be stored in our variable answer .

We wrapped the prompt call in a parseInt function so that a number is returned from the user’s input.

With that done, let’s move on to the next subproblem: “Loop from 1 to the entered number”. There are many ways to do this in JavaScript. One of the common ways - that you actually see in many other languages like Java, C++, and Ruby - is with the for loop :

If you haven’t seen this before and it looks strange, it’s actually straightforward. We declare a variable i and assign it 1: the initial value of the variable i in our loop. The second clause, i <= answer is our condition. We want to loop until i is greater than answer . The third clause, i++ , tells our loop to increment i by 1 every iteration. As a result, if the user inputs 10, this loop would print numbers 1 - 10 to the console.

Most of the time, programmers find themselves looping from 0. Due to the needs of our program, we’re starting from 1

With that working, let’s move on to the next problem: If the current number is divisible by 3, then print Fizz .

We are using the modulus operator ( % ) here to divide the current number by three. If you recall from a previous lesson, the modulus operator returns the remainder of a division. So if a remainder of 0 is returned from the division, it means the current number is divisible by 3.

After this change the program will now output this when you run it and the user inputs 10:

The program is starting to take shape. The final few subproblems should be easy to solve as the basic structure is in place and they are just different variations of the condition we’ve already got in place. Let’s tackle the next one: If the current number is divisible by 5 then print Buzz .

When you run the program now, you should see this output if the user inputs 10:

We have one more subproblem to solve to complete the program: If the current number is divisible by 3 and 5 then print FizzBuzz .

We’ve had to move the conditionals around a little to get it to work. The first condition now checks if i is divisible by 3 and 5 instead of checking if i is just divisible by 3. We’ve had to do this because if we kept it the way it was, it would run the first condition if (i % 3 === 0) , so that if i was divisible by 3, it would print Fizz and then move on to the next number in the iteration, even if i was divisible by 5 as well.

With the condition if (i % 3 === 0 && i % 5 === 0) coming first, we check that i is divisible by both 3 and 5 before moving on to check if it is divisible by 3 or 5 individually in the else if conditions.

The program is now complete! If you run it now you should get this output when the user inputs 20:

• Read How to Think Like a Programmer - Lessons in Problem Solving by Richard Reis.
• Watch How to Begin Thinking Like a Programmer by Coding Tech. It’s an hour long but packed full of information and definitely worth your time watching.
• Read this Pseudocode: What It Is and How to Write It article from Built In.

Knowledge check

This section contains questions for you to check your understanding of this lesson on your own. If you’re having trouble answering a question, click it and review the material it links to.

• What are the three stages in the problem solving process?
• Why is it important to clearly understand the problem first?
• What can you do to help get a clearer understanding of the problem?
• What are some of the things you should do in the planning stage of the problem solving process?
• What is an algorithm?
• What is pseudocode?
• What are the advantages of breaking a problem down and solving the smaller problems?

Additional resources

This section contains helpful links to other content. It isn’t required, so consider it supplemental.

• Read the first chapter in Think Like a Programmer: An Introduction to Creative Problem Solving ( not free ). This book’s examples are in C++, but you will understand everything since the main idea of the book is to teach programmers to better solve problems. It’s an amazing book and worth every penny. It will make you a better programmer.
• Watch this video on repetitive programming techniques .
• Watch Jonathan Blow on solving hard problems where he gives sage advice on how to approach problem solving in software projects.

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Lesson 27 of 34 By Hemant Deshpande

Table of Contents

Coding and Programming skills hold a significant and critical role in implementing and developing various technologies and software. They add more value to the future and development. These programming and coding skills are essential for every person to improve problem solving skills. So, we brought you this article to help you learn and know the importance of these skills in the future. 

Want a Top Software Development Job? Start Here!

Topics covered in this problem solving in programming article are:

• What is Problem Solving in Programming? 
• Problem Solving skills in Programming
• How does it impact your career ?
• Steps involved in Problem Solving
• Steps to improve Problem Solving in programming

What is Problem Solving in Programming?

Computers are used to solve various problems in day-to-day life. Problem Solving is an essential skill that helps to solve problems in programming. There are specific steps to be carried out to solve problems in computer programming, and the success depends on how correctly and precisely we define a problem. This involves designing, identifying and implementing problems using certain steps to develop a computer.

When we know what exactly problem solving in programming is, let us learn how it impacts your career growth.

How Does It Impact Your Career?

Many companies look for candidates with excellent problem solving skills. These skills help people manage the work and make candidates put more effort into the work, which results in finding solutions for complex problems in unexpected situations. These skills also help to identify quick solutions when they arise and are identified. 

People with great problem solving skills also possess more thinking and analytical skills, which makes them much more successful and confident in their career and able to work in any kind of environment. 

The above section gives you an idea of how problem solving in programming impacts your career and growth. Now, let's understand what problem solving skills mean.

Problem Solving Skills in Programming

Solving a question that is related to computers is more complicated than finding the solutions for other questions. It requires excellent knowledge and much thinking power. Problem solving in programming skills is much needed for a person and holds a major advantage. For every question, there are specific steps to be followed to get a perfect solution. By using those steps, it is possible to find a solution quickly.

The above section is covered with an explanation of problem solving in programming skills. Now let's learn some steps involved in problem solving.

Steps Involved in Problem Solving

Before being ready to solve a problem, there are some steps and procedures to be followed to find the solution. Let's have a look at them in this problem solving in programming article.

Basically, they are divided into four categories:

• Analysing the problem
• Developing the algorithm
• Testing and debugging

Analysing the Problem

Every problem has a perfect solution; before we are ready to solve a problem, we must look over the question and understand it. When we know the question, it is easy to find the solution for it. If we are not ready with what we have to solve, then we end up with the question and cannot find the answer as expected. By analysing it, we can figure out the outputs and inputs to be carried out. Thus, when we analyse and are ready with the list, it is easy and helps us find the solution easily. 

Developing the Algorithm

It is required to decide a solution before writing a program. The procedure of representing the solution  in a natural language called an algorithm. We must design, develop and decide the final approach after a number of trials and errors, before actually writing the final code on an algorithm before we write the code. It captures and refines all the aspects of the desired solution.

Once we finalise the algorithm, we must convert the decided algorithm into a code or program using a dedicated programming language that is understandable by the computer to find a desired solution. In this stage, a wide variety of programming languages are used to convert the algorithm into code. 

Testing and Debugging

The designed and developed program undergoes several rigorous tests based on various real-time parameters and the program undergoes various levels of simulations. It must meet the user's requirements, which have to respond with the required time. It should generate all expected outputs to all the possible inputs. The program should also undergo bug fixing and all possible exception handling. If it fails to show the possible results, it should be checked for logical errors.

Industries follow some testing methods like system testing, component testing and acceptance testing while developing complex applications. The errors identified while testing are debugged or rectified and tested again until all errors are removed from the program.

The steps mentioned above are involved in problem solving in programming. Now let's see some more detailed information about the steps to improve problem solving in programming.

Steps to Improve Problem Solving in Programming

Right mindset.

The way to approach problems is the key to improving the skills. To find a solution, a positive mindset helps to solve problems quickly. If you think something is impossible, then it is hard to achieve. When you feel free and focus with a positive attitude, even complex problems will have a perfect solution.

Making Right Decisions

When we need to solve a problem, we must be clear with the solution. The perfect solution helps to get success in a shorter period. Making the right decisions in the right situation helps to find the perfect solution quickly and efficiently. These skills also help to get more command over the subject.

Keeping Ideas on Track

Ideas always help much in improving the skills; they also help to gain more knowledge and more command over things. In problem solving situations, these ideas help much and help to develop more skills. Give opportunities for the mind and keep on noting the ideas.

Learning from Feedbacks

A crucial part of learning is from the feedback. Mistakes help you to gain more knowledge and have much growth. When you have a solution for a problem, go for the feedback from the experienced or the professionals. It helps you get success within a shorter period and enables you to find other solutions easily.

Asking Questions

Questions are an incredible part of life. While searching for solutions, there are a lot of questions that arise in our minds. Once you know the question correctly, then you are able to find answers quickly. In coding or programming, we must have a clear idea about the problem. Then, you can find the perfect solution for it. Raising questions can help to understand the problem.

These are a few reasons and tips to improve problem solving in programming skills. Now let's see some major benefits in this article.

• Problem solving in programming skills helps to gain more knowledge over coding and programming, which is a major benefit.
• These problem solving skills also help to develop more skills in a person and build a promising career.
• These skills also help to find the solutions for critical and complex problems in a perfect way.
• Learning and developing problem solving in programming helps in building a good foundation.
• Most of the companies are looking for people with good problem solving skills, and these play an important role when it comes to job opportunities 

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Problem solving in programming skills is important in this modern world; these skills build a great career and hold a great advantage. This article on problem solving in programming provides you with an idea of how it plays a massive role in the present world. In this problem solving in programming article, the skills and the ways to improve more command on problem solving in programming are mentioned and explained in a proper way.

If you are looking to advance in your career. Simplilearn provides training and certification courses on various programming languages - Python , Java , Javascript , and many more. Check out our Post Graduate Program in Full Stack Web Development course that will help you excel in your career.

If you have any questions for us on the problem solving in programming article. Do let us know in the comments section below; we have our experts answer it right away.

About the Author

Hemant Deshpande, PMP has more than 17 years of experience working for various global MNC's. He has more than 10 years of experience in managing large transformation programs for Fortune 500 clients across verticals such as Banking, Finance, Insurance, Healthcare, Telecom and others. During his career he has worked across the geographies - North America, Europe, Middle East, and Asia Pacific. Hemant is an internationally Certified Executive Coach (CCA/ICF Approved) working with corporate leaders. He also provides Management Consulting and Training services. He is passionate about writing and regularly blogs and writes content for top websites. His motto in life - Making a positive difference.

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Effective Problem-Solving and Decision-Making

This course is part of multiple programs. Learn more

This course is part of multiple programs

Taught in English

Some content may not be translated

Instructor: Diane Spiegel

Financial aid available

248,458 already enrolled

(6,669 reviews)

What you'll learn

Explain both the affordances and limitations associated with problem-solving and decision-making

Reflect on how mindset and personal bias influence your ability to solve problems and make decisions

Explain and discuss how organizational decisions or non-decisions impact personal development, team dynamics, and company-wide performance

Articulate how both good and bad team decisions can benefit your professional growth

Skills you'll gain

• Critical Thinking
• Decision Theory
• Decision-Making
• Problem Solving

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There are 4 modules in this course

Problem-solving and effective decision-making are essential skills in today’s fast-paced and ever-changing workplace. Both require a systematic yet creative approach to address today’s business concerns. This course will teach an overarching process of how to identify problems to generate potential solutions and how to apply decision-making styles in order to implement and assess those solutions. Through this process, you will gain confidence in assessing problems accurately, selecting the appropriate decision-making approaches for the situation at hand, making team decisions, and measuring the success of the solution’s implementation. Using case studies and situations encountered by class members, you will explore proven, successful problem-solving and decision-making models and methods that can be readily transferred to workplace projects.

Upon completing this course, you will be able to: 1. Identify key terms, styles, and approaches to effective problem-solving and decision-making 2. Explain both the affordances and limitations associated with problem-solving and decision-making 3. Reflect on how mindset and personal bias influence your ability to solve problems and make decisions 4. Explain and discuss how organizational decisions or non-decisions impact personal development, team dynamics, and company-wide performance 5. Articulate how both good and bad team decisions can benefit your professional growth

Identify the Problem

Problem-solving is an essential skill in today's fast-paced and ever-changing workplace. It requires a systematic approach that incorporates effective decision-making. Throughout this course, we will learn an overarching process of identifying problems to generate potential solutions, then apply decision-making styles in order to implement and assess those solutions. In this module, we will learn to identify problems by using a root cause approach as a foundational tool. Additionally, we will address problem parameters that often occur in business situations. Throughout this course, we will utilize a case scenario that will provide specific examples to illustrate the steps in the problem-solving and decision-making process.

What's included

1 video 7 readings 1 quiz 1 discussion prompt

1 video • Total 5 minutes

• Accurately Identify the Problem • 5 minutes • Preview module

7 readings • Total 55 minutes

• Problem Solving in Today’s Workplace • 5 minutes
• Introduction: Problem-Solving and Decision-Making Process • 10 minutes
• The Problem-Solving and Decision-Making Process • 5 minutes
• Course Example: Hybrid Work Environment • 5 minutes
• Parameters • 10 minutes
• Identify the Problem • 15 minutes
• Review: Identify the Problem • 5 minutes

1 quiz • Total 30 minutes

• Module 1 Quiz • 30 minutes

1 discussion prompt • Total 30 minutes

• Benefits and Drawbacks of Problem-Solving and Decision-Making Process • 30 minutes

Generate Solutions

In the previous module, we learned how to identify the root cause of a problem. Now we will discuss how mindset and personal bias can potentially limit creativity in solving workplace challenges. We’ll review problem-solving styles and creativity enhancement approaches to generate a variety of unique solutions while addressing constraints and limited resources.

1 video 6 readings 1 quiz 1 discussion prompt

1 video • Total 4 minutes

• Generate Multiple Solutions with Various Team Perspectives • 4 minutes • Preview module

6 readings • Total 80 minutes

• Introduction • 5 minutes
• Mindset & Personal Bias • 10 minutes
• Problem Solving Styles • 20 minutes
• Generate Solutions • 30 minutes
• Generate Solutions: Hybrid Work Environment Example • 10 minutes
• Review: Generate Solutions • 5 minutes
• Module 2 Quiz • 30 minutes
• Mindset & Personal Bias • 30 minutes

Make the Decision

In the previous module, we learned how to generate a variety of creative solutions. Now we need to decide which solution is the best option. We will explore which decision-making styles lend themselves to best solve the problem given its affordances and limitations. Tips for making better decisions are outlined as well as hazards to avoid.

1 video 5 readings 1 quiz 1 discussion prompt

1 video • Total 3 minutes

• Make the Decision • 3 minutes • Preview module

5 readings • Total 55 minutes

• Decisions Making Styles • 10 minutes
• Choose a Solution • 20 minutes
• Make the Decision: Hybrid Work Environment Example • 10 minutes
• Review: Make the Decision • 10 minutes
• Module 3 Quiz • 30 minutes
• The Impact of Decisions • 30 minutes

Implement and Assess the Solution

In the previous module, we learned how to make the decision given the best information at hand. Once the decision is made, it’s time to implement and assess the chosen solution. As we get ready to implement, we are well-served to review situational variables as elements in the environment may have shifted during the decision-making process. We will also need to define the solution’s performance metrics and Key Performance Indicators (KPIs) in order to later measure or assess the solution’s impact on the organization. Anecdotal data is equally valuable as it can share the emotional impact on employees.

• Measure Success Through Data • 3 minutes • Preview module
• Implement the Solution • 30 minutes
• Assess the Solution • 10 minutes
• Review: Implement and Assess the Solution • 5 minutes
• Final Message • 5 minutes
• Module 4 Quiz • 30 minutes
• Implement & Assess the Solution • 30 minutes

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thanks to this course i been more enhance my skill of problem solving in my profession and using different technique to solve the problems and making the best decission making

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The Training course was very attractive and valuable for implementation of the knowledge gain for solving of different problem faced in official works as well as personal responsibilities.

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What is Programming? A Handbook for Beginners

Welcome to the amazing world of programming. This is one of the most useful and powerful skills that you can learn and use to make your visions come true.

In this handbook, we will dive into why programming is important, its applications, its basic concepts, and the skills you need to become a successful programmer.

You will learn:

• What programming is and why it is important .
• What a programming language is and why it is important .
• How programming is related to binary numbers .
• Real-world applications of programming .
• Skills you need to succeed as a programmer .
• Tips for learning how to code .
• Basic programming concepts .
• Types of programming languages .
• How to contribute to open source projects .
• And more...

Are you ready? Let's begin! ✨  

🔹 What is Programming?

Did you know that computer programming is already a fundamental part of your everyday lives? Let's see why. I'm sure that you will be greatly surprised.

Every time you turn on your smartphone, laptop, tablet, smart TV, or any other electronic device, you are running code that was planned, developed, and written by developers. This code creates the final and interactive result that you can see on your screen.

That is exactly what programming is all about. It is the process of writing code to solve a particular problem or to implement a particular task.

Programming is what allows your computer to run the programs you use every day and your smartphone to run the apps that you love. It is an essential part of our world as we know it.

Whenever you check your calendar, attend virtual conferences, browse the web, or edit a document, you are using code that has been written by developers.

"And what is code?" you may ask.

Code is a sequence of instructions that a programmer writes to tell a device (like a computer) what to do.

The device cannot know by itself how to handle a particular situation or how to perform a task. So developers are in charge of analyzing the situation and writing explicit instructions to implement what is needed.

To do this, they follow a particular syntax (a set of rules for writing the code).

A developer (or programmer) is the person who analyzes a problem and implements a solution in code.

Sounds amazing, right? It's very powerful and you can be part this wonderful world too by learning how to code. Let's see how.

You, as a developer.

Let's put you in a developer's shoes for a moment. Imagine that you are developing a mobile app, like the ones that you probably have installed on your smartphone right now.

What is the first thing that you would do?

Think about this for a moment.

The answer is...

Analyzing the problem. What are you trying to build?

As a developer, you would start by designing the layout of the app, how it will work, its different screens and functionality, and all the small details that will make your app an awesome tool for users around the world.

Only after you have everything carefully planned out, you can start to write your code. To do that, you will need to choose a programming language to work with. Let's see what a programming language is and why they are super important.

🔸 What is a Programing Language?

A programming language is a language that computers can understand.

We cannot just write English words in our program like this:

"Computer, solve this task!"

and hope that our computer can understand what we mean. We need to follow certain rules to write the instructions.

Every programming language has its own set of rules that determine if a line of code is valid or not. Because of this, the code you write in one programming language will be slightly different from others.

💡 Tip: Some programming languages are more complex than others but most of them share core concepts and functionality. If you learn how to code in one programming language, you will likely be able to learn another one faster.

Before you can start writing awesome programs and apps, you need to learn the basic rules of the programming language you chose for the task.

💡 Tip: a program is a set of instructions written in a programming language for the computer to execute. We usually write the code for our program in one or multiple files.

For example, this is a line of code in Python (a very popular programming language) that shows the message "Hello, World!" :

But if we write the same line of code in JavaScript (a programming language mainly used for web development), we will get an error because it will not be valid.

To do something very similar in JavaScript, we would write this line of code instead:

Visually, they look very different, right? This is because Python and JavaScript have a different syntax and a different set of built-in functions .

💡 Tip : built-in functions are basically tasks that are already defined in the programming language. This lets us use them directly in our code by writing their names and by specifying the values they need.  

In our examples, print() is a built-in function in Python while console.log() is a function that we can use in JavaScript to see the message in the console (an interactive tool) if we run our code in the browser.

Examples of programming languages include Python, JavaScript, TypeScript, Java, C, C#, C++, PHP, Go, Swift, SQL, and R. There are many programming languages and most of them can be used for many different purposes.

💡 Tip: These were the most popular programming languages on the Stack Overflow Developer Survey 2022 :

There are many other programming languages (hundreds or even thousands!) but usually, you will learn and work with some of the most popular ones. Some of them have broader applications like Python and JavaScript while others (like R) have more specific (and even scientific) purposes.

This sounds very interesting, right? And we are only starting to talk about programming languages. There is a lot to learn about them and I promise you that if you dive deeper into programming, your time and effort will be totally worth it.

Awesome! Now that you know what programming is and what programming languages are all about, let's see how programming is related to binary numbers.

🔹 Programming and Binary Numbers

When you think about programming, perhaps the first thing that comes to your mind is something like the below image, right? A sequence of 0 s and 1 s on your computer.

Programming is indeed related to binary numbers ( 0 and 1 ) but in an indirect way. Developers do not actually write their code using zeros and ones.

We usually write programs in a high-level programming language, a programming language with a syntax that recognizes specific words (called keywords), symbols, and values of different data types.

Basically, we write code in a way that humans can understand.

For example, these are the keywords that we can use in Python:

Every programming language has its own set of keywords (words written in English). These keywords are part of the syntax and core functionality of the programming language.

But keywords are just common words in English, almost like the ones that we would find in a book.

That leads us to two very important questions:

• How does the computer understand and interpret what we are trying to say?
• Where does the binary number system come into play here?

The computer does not understand these words, symbols, or values directly.

When a program runs, the code that we write in a high-level programming language that humans can understand is automatically transformed into binary code that the computer can understand.

This transformation of source code that humans can understand into binary code that the computer can understand is called compilation .

According to Britannica , a compiler is defined as:

Computer software that translates (compiles) source code written in a high-level language (e.g., C++) into a set of machine-language instructions that can be understood by a digital computer’s CPU.

Britannica also mentions that:

The term compiler was coined by American computer scientist Grace Hopper , who designed one of the first compilers in the early 1950s.

Some programming languages can be classified as compiled programming languages while others can be classified as interpreted programming languages based on how to they are transformed into machine-language instructions.

However, they all have to go through a process that converts them into instructions that the computer can understand.

Awesome. Now you know why binary code is so important for computer science. Without it, basically programming would not exist because computers would not be able to understand our instructions.

Now let's dive into the applications of programming and the different areas that you can explore.

🔸 Real-World Applications of Programming

Programming has many different applications in many different industries. This is truly amazing because you can apply your knowledge in virtually any industry that you are interested in.

From engineering to farming, from game development to physics, the possibilities are endless if you learn how to code.  

Let's see some of them. (I promise you. They are amazing! ⭐) .

Front-End Web Development

If you learn how to code, you can use your programming skills to design and develop websites and online platforms. Front-End Web Developers create the parts of the websites that users can see and interact with directly.

For example, right now you are reading an article on freeCodeCamp 's publication. The publication looks like this and it works like this thanks to code that front-end web developers wrote line by line.

💡 Tip: If you learn front-end web development, you can do this too.

Front-End Web Developers use HTML and CSS to create the structure of the website (these are markup languages, which are used to present information) and they write JavaScript code to add functionality and interactivity.

If you are interested in learning front-end web development, you can learn HTML and CSS with these free courses on freeCodeCamp's YouTube Channel:

• Learn HTML5 and CSS3 From Scratch - Full Course
• Learn HTML & CSS – Full Course for Beginners
• Frontend Web Development Bootcamp Course (JavaScript, HTML, CSS)
• Introduction To Responsive Web Design - HTML & CSS Tutorial

You can also learn JavaScript for free with these free online courses:

• Learn JavaScript - Full Course for Beginners
• JavaScript Programming - Full Course
• JavaScript DOM Manipulation – Full Course for Beginners
• Learn JavaScript by Building 7 Games - Full Course

💡 Tip: You can also earn a Responsive Web Design Certification while you learn with interactive exercises on freeCodeCamp.

Back-End Web Development

More complex and dynamic web applications that work with user data also require a server . This is a computer program that receives requests and sends appropriate responses. They also need a database , a collection of values stored in a structured way.

Back-End Web Developers are in charge of developing the code for these servers. They decide how to handle the different requests, how to send appropriate resources, how to store the information, and basically how to make everything that runs behind the scenes work smoothly and efficiently.

A real-world example of back-end web development is what happens when you create an account on freeCodeCamp and complete a challenge. Your information is stored on a database and you can access it later when you sign in with your email and password.

This amazing interactive functionality was implemented by back-end web developers.

💡 Tip: Full-stack Web Developers are in charge of both Front-End and Back-End Web Development. They have specialized knowledge on both areas.

All the complex platforms that you use every day, like social media platforms, online shopping platforms, and educational platforms, use servers and back-end web development to power their amazing functionality.

Python is an example of a powerful programming language used for this purpose. This is one of the most popular programming languages out there, and its popularity continues to rise every year. This is partly because it is simple and easy to learn and yet powerful and versatile enough to be used in real-world applications.

💡 Tip: if you are curious about the specific applications of Python, this is an article I wrote on this topic .

JavaScript can also be used for back-end web development thanks to Node.js.

Other programming languages used to develop web servers are PHP, Ruby, C#, and Java.

If you would like to learn Back-End Web Development, these are free courses on freeCodeCamp's YouTube channel:

• Python Backend Web Development Course (with Django)
• Node.js and Express.js - Full Course
• Full Stack Web Development for Beginners (Full Course on HTML, CSS, JavaScript, Node.js, MongoDB)
• Node.js / Express Course - Build 4 Projects

💡 Tip: freeCodeCamp also has a free Back End Development and APIs certification.

Mobile App Development

Mobile apps have become part of our everyday lives. I'm sure that you could not imagine life without them.

Think about your favorite mobile app. What do you love about it?

Our favorite apps help us with our daily tasks, they entertain us, they solve a problem, and they help us to achieve our goals. They are always there for us.

That is the power of mobile apps and you can be part of this amazing world too if you learn mobile app development.

Developers focused on mobile app development are in charge of planning, designing, and developing the user interface and functionality of these apps. They identify a gap in the existing apps and they try to create a working product to make people's lives better.

💡 Tip: regardless of the field you choose, your goal as a developer should always be making people's lives better. Apps are not just apps, they have the potential to change our lives. You should always remember this when you are planning your projects. Your code can make someone's life better and that is a very important responsibility.

Mobile app developers use programming languages like JavaScript, Java, Swift, Kotlin, and Dart. Frameworks like Flutter and React Native are super helpful to build cross-platform mobile apps (that is, apps that run smoothly on multiple different operating systems like Android and iOS).

According to Flutter 's official documentation:

Flutter is an open source framework by Google for building beautiful, natively compiled, multi-platform applications from a single codebase.

If you would like to learn mobile app development, these are free courses that you can take on freeCodeCamp's YouTube channel:

• Flutter Course for Beginners – 37-hour Cross Platform App Development Tutorial
• Flutter Course - Full Tutorial for Beginners (Build iOS and Android Apps)
• React Native - Intro Course for Beginners
• Learn React Native Gestures and Animations - Tutorial

Game Development

Games create long-lasting memories. I'm sure that you still remember your favorite games and why you love (or loved) them so much. Being a game developer means having the opportunity of bringing joy and entertainment to players around the world.

Game developers envision, design, plan, and implement the functionality of a game. They also need to find or create assets such as characters, obstacles, backgrounds, music, sound effects, and more.

💡 Tip: if you learn how to code, you can create your own games. Imagine creating an awesome and engaging game that users around the world will love. That is what I personally love about programming. You only need your computer, your knowledge, and some basic tools to create something amazing.

Popular programming languages used for game development include JavaScript, C++, Python, and C#.

If you are interested in learning game development, you can take these free courses on freeCodeCamp's YouTube channel:

• JavaScript Game Development Course for Beginners
• Learn Unity - Beginner's Game Development Tutorial
• Learn Python by Building Five Games - Full Course
• Code a 2D Game Using JavaScript, HTML, and CSS (w/ Free Game Assets) – Tutorial
• 2D Game Development with GDevelop - Crash Course
• Pokémon Coding Tutorial - CS50's Intro to Game Development

Biology, Physics, and Chemistry

Programming can be applied in every scientific field that you can imagine, including biology, physics, chemistry, and even astronomy. Yes! Scientists use programming all the time to collect and analyze data. They can even run simulations to test hypotheses.

In biology, computer programs can simulate population genetics and population dynamics. There is even an entire field called bioinformatics .

According to this article "Bioinformatics" by Ardeshir Bayat, member of the Centre for Integrated Genomic Medical Research at the University of Manchester:

Bioinformatics is defined as the application of tools of computation and analysis to the capture and interpretation of biological data.

Dr. Bayat mentions that bioinformatics can be used for genome sequencing. He also mentions that its discoveries may lead to drug discoveries and individualized therapies.

Frequently used programming languages for bioinformatics include Python, R, PHP, PERL, and Java.

💡 Tip: R is a programming "language and environment for statistical computing and graphics" ( source ).

An example of a great tool that scientists can use for biology is Biopython . This is a Python framework with "freely available tools for biological computation."

If you would like to learn more about how you can apply your programming skills in science, these are free courses that you can take on freeCodeCamp's YouTube channel:

• Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis
• R Programming Tutorial - Learn the Basics of Statistical Computing
• Learn Python - Full Course for Beginners [Tutorial]

Physics requires running many simulations and programming is perfect for doing exactly that. With programming, scientists can program and run simulations based on specific scenarios that would be hard to replicate in real life. This is much more efficient.

Programming languages that are commonly used for physics simulations include C, Java, Python, MATLAB, and JavaScript.  

Chemistry also relies on simulations and data analysis, so it's a field where programming can be a very helpful tool.

In this scientific article by Dr. Ivar Ugi and his colleagues from Organisch-chemisches Institut der Technischen Universität München, they mention that:

The design of entirely new syntheses, and the classification and documentation of structures, substructures, and reactons are examples of new applications of computers to chemistry.

Scientific experiments also generate detailed data and results that can be analyzed with computer programs developed by scientists.  

Think about it: writing a program to generate a box plot or a scatter plot or any other type of plot to visualize trends in thousands of measurements can save researchers a lot of time and effort. This lets them focus on the most important part of their work: analyzing the results.

💡 Tips: if you are interested in diving deeper into this, this is a list of chemistry simulations by the American Chemical Society. These simulations were programmed by developers and they are helping thousands of students and teachers around the world.

Think about it...You could build the next great simulation. If you are interested in a scientific field, I totally recommend learning how to code. Your work will be much more productive and your results will be easier to analyze.

If you are interested in learning programming for scientific applications, these are free courses on freeCodeCamp's YouTube channel:

• Python for Data Science - Course for Beginners (Learn Python, Pandas, NumPy, Matplotlib)

Data Science and Engineering

Talking about data...programming is also essential for a field called Data Science . If you are interested in answering questions through data and statistics, this field might be exactly what you are looking for and having programming skills will help you to achieve your goals.

Data scientists collect and analyze data in order to answer questions in many different fields. According to UC Berkeley in the article " What is Data Science? ":

Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions.

There are many powerful programming languages for analyzing and visualizing data, but perhaps one of the most frequently used ones for this purpose is Python.

This is an example of the type of data visualizations that you can create with Python. They are very helpful to analyze data visually and you can customize them to your fit needs.

If you are interested in learning programming for data science, these are free courses on freeCodeCamp's YouTube channel:

• Learn Data Science Tutorial - Full Course for Beginners
• Intro to Data Science - Crash Course for Beginners
• Build 12 Data Science Apps with Python and Streamlit - Full Course
• Data Analysis with Python - Full Course for Beginners (Numpy, Pandas, Matplotlib, Seaborn)

💡 Tip: you can also earn these free certifications on freeCodeCamp:

• Data Visualization
• Data Analysis with Python

Engineering

Engineering is another field where programming can help you to succeed. Being able to write your own computer programs can make your work much more efficient.

There are many tools created specifically for engineers. For example, the R programming language is specialized in statistical applications and Python is very popular in this field too.

Another great tool for programming in engineering is MATLAB . According to its official website:

MATLAB is a programming and numeric computing platform used by millions of engineers and scientists to analyze data, develop algorithms, and create models.

Really, the possibilities are endless.

You can learn MATLAB with this crash course on the freeCodeCamp YouTube channel .

If you are interested in learning engineering tools related to programming, this is a free course on freeCodeCamp's YouTube channel that covers AutoCAD, a 2D and 3D computer-aided design software used by engineers:

• AutoCAD for Beginners - Full University Course

Medicine and Pharmacology

Medicine and pharmacology are constantly evolving by finding new treatments and procedures. Let's see how you can apply your programming skills in these fields.

Programming is really everywhere. If you are interested in the field of medicine, learning how to code can be very helpful for you too. Even if you would like to focus on computer science and software development, you can apply your knowledge in both fields.

Specialized developers are in charge of developing and writing the code that powers and controls the devices and machines that are used by modern medicine.

Think about it...all these machines and devices are controlled by software and someone has to write that software. Medical records are also stored and tracked by specialized systems created by developers. That could be you if you decide to follow this path. Sounds exciting, right?

According to the scientific article Application of Computer Techniques in Medicine :

Major uses of computers in medicine include hospital information system, data analysis in medicine, medical imaging laboratory computing, computer assisted medical decision making, care of critically ill patients, computer assisted therapy and so on.

Pharmacology

Programming and computer science can also be applied to develop new drugs in the field of pharmacology.

A remarkable example of what you can achieve in this field by learning how to code is presented in this article by MIT News. It describes how an MIT senior, Kristy Carpenter, was using computer science in 2019 to develop "new, more affordable drugs." Kristy mentions that:

Artificial intelligence, which can help compute the combinations of compounds that would be better for a particular drug, can reduce trial-and-error time and ideally quicken the process of designing new medicines.

Another example of a real-world application of programming in pharmacology is related to Python (yes, Python has many applications!). Among its success stories , we find that Python was selected by AstraZeneca to develop techniques and programs that can help scientists to discover new drugs faster and more efficiently.

The documentation explains that:

To save time and money on laboratory work, experimental chemists use computational models to narrow the field of good drug candidates, while also verifying that the candidates to be tested are not simple variations of each other's basic chemical structure.

If you are interested in learning programming for medicine or health-related fields, this is a free course on freeCodeCamp's YouTube channel on programming for healthcare imaging:

• PyTorch and Monai for AI Healthcare Imaging - Python Machine Learning Course

Have you ever thought that programming could be helpful for education? Well, let me tell you that it is and it is very important. Why? Because the digital learning tools that students and teachers use nowadays are programmed by developers.

Every time a student opens an educational app, browses an educational platform like freeCodeCamp, writes on a digital whiteboard, or attends a class through an online meeting platform, programming is making that possible.

As a programmer or as a teacher who knows how to code, you can create the next great app that will enhance the learning experience of students around the world.

Perhaps it will be a note-taking app, an online learning platform, a presentation app, an educational game, or any other app that could be helpful for students.

The important thing is to create it with students in mind if your goal is to make something amazing that will create long-lasting memories.

If you envision it, then you can create it with code.  

Teachers can also teach their students how to code to develop their problem-solving skills and to teach them important skills for their future.

💡 Tip: if you are teaching students how to code, Scratch is a great programming language to teach the basics of programming. It is particularly focused on teaching children how to code in an interactive way.

According to the official Scratch website:

Scratch is the world’s largest coding community for children and a coding language with a simple visual interface that allows young people to create digital stories, games, and animations.

If you are interested in learning how to code for educational purposes, these are courses that you may find helpful on freeCodeCamp's YouTube channel:

• Scratch Tutorial for Beginners - Make a Flappy Bird Game
• Computational Thinking & Scratch - Intro to Computer Science - Harvard's CS50 (2018)
• Android Development for Beginners - Full Course

Machine Learning, Artificial Intelligence, and Robotics

Some of the most amazing fields that are directly related to programming are Machine Learning, Artificial Intelligence, and Robotics. Let's see why.

Artificial Intelligence is defined by Britannica as:

The project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.

Machine learning is a branch or a subset of the field of Artificial Intelligence in which systems can learn on their own based on data. The goal of this learning process is to predict the expected output. These models continuously learn how to "think" and how to analyze situations based on their previous training.

The most commonly used programming languages in these fields are Python, C, C#, C++, and MATLAB.

Artificial intelligence and Machine Learning have amazing applications in various industries, such as:

• Image and object detection.
• Making predictions based on patterns.
• Text recognition.
• Recommendation engines (like when an online shopping platform shows you products that you may like or when YouTube shows you videos that you may like).
• Spam detection for emails.
• Fraud detection.
• Social media features like personalized feeds.
• Many more... there are literally millions of applications in virtually every industry.

If you are interested in learning how to code for Artificial Intelligence and Machine Learning, these are free courses on freeCodeCamp's YouTube channel:

• Machine Learning for Everybody – Full Course
• Machine Learning Course for Beginners
• PyTorch for Deep Learning & Machine Learning – Full Course
• TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
• Self-Driving Car with JavaScript Course – Neural Networks and Machine Learning
• Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial
• Practical Deep Learning for Coders - Full Course from fast.ai and Jeremy Howard
• Deep Learning Crash Course for Beginners
• Advanced Computer Vision with Python - Full Course

💡 Tip: you can also earn a Machine Learning with Python Certification on freeCodeCamp.

Programming is also very important for robotics. Yes, robots are programmed too!

Robotics is defined by Britannica as the:

Design, construction, and use of machines (robots) to perform tasks done traditionally by human beings.

Robots are just like computers. They do not know what to do until you tell them what to do by writing instructions in your programs. If you learn how to code, you can program robots and industrial machinery found in manufacturing facilities.

If you are interested in learning how to code for robotics, electronics, and related fields, this is a free course on Arduino on freeCodeCamp's YouTube channel:

• Arduino Course for Beginners - Open-Source Electronics Platform

Other Applications

There are many other fascinating applications of programming in almost every field. These are some highlights:

• Agriculture: in this article by MIT News, a farmer developed an autonomous tractor app after learning how to code.
• Self-driving cars: autonomous cars rely on software to analyze their surroundings and to make quick and accurate decisions on the road. If you are interested in this area, this is a course on this topic on freeCodeCamp's YouTube channel.
• Finance: programming can also be helpful to develop programs and models that predict financial indicators and trends. For example, this is a course on algorithmic trading on freeCodeCamp's YouTube channel.

The possibilities are endless. I hope that this section will give you a notion of why learning how to code is so important for your present and for your future. It will be a valuable skill to have in any field you choose.

Awesome. Now let's dive into the soft skills that you need to become a successful programmer.

🔹 Skills of a Successful Programmer

After going through the diverse range of applications of programming, you must be curious to know what skills are needed to succeed in this field.

A programmer should be curious. Whether you are just starting to learn how to code or you already have 20 years of experience, coding projects will always present you with new challenges and learning opportunities. If you take these opportunities, you will continously improve your skills and succeed.

Enthusiasm is a key trait of a successful programmer but this applies in general to any field if you want to succeed. Enthusiasm will keep you happy and curious about what you are creating and learning.

💡 Tip: If you ever feel like you are not as enthusiastic as you used to be, it's time to find or learn something new that can light the spark in you again and fill you with hope and dreams.

A programmer must be patient because transforming an initial idea into a working product can take time, effort, and many different steps. Patience will keep you focused on your final goal.  

Programming can be challenging. That is true. But what defines you is not how many challenges you face, it's how you face them. If you thrive despite these challenges, you will become a better programmer and you could create something that could change the world.

Programmers must be creative because even though every programming language has a particular set of rules for writing the code, coding is like using LEGOs. You have the building-blocks but you need to decide what to create and how to create it. The process of writing the code requires creativity while following the established best practices.

Problem-solving and Analysis

Programming is basically analyzing and solving problems with code. Depending on your field of choice, those problems will be simpler or more complex but they will all require some level of problem-solving skills and a thorough analysis of the situation.

Questions like:

• What should I build?
• How can I build it?
• What is the best way to build this?

Are part of the everyday routine of a programmer.

Ability to Focus for Long Periods of Time

When you are working on a coding project, you will need to focus on a task for long periods of time. From creating the design, to planning and writing the code, to testing the result, and to fixing bugs (issues with the code), you will dedicate many hours to a particular task. This is why it's essential to be able to focus and to keep your final goal in mind.

Taking Detailed Notes

This skill is very important for programmers, particularly when you are learning how to code. Taking detailed notes can be help you to understand and remember the concepts and tools you learn. This also applies for experienced programmers, since being a programmer involves life-long learning.

Communication

Initially, you might think that programming is a solitary activity and imagine that a programmer spends hundreds of hours alone sitting on a desk.

But the reality is that when you find your first job, you will see that communication is super important to coordinate tasks with other team members and to exchange ideas and feedback.

Open to Feedback

In programming, there is usually more than one way to implement the same functionality. Different alternatives may work similarly, but some may be easier to read or more efficient in terms of time or resource consumption.

When you are learning how to code, you should always take constructive feedback as a tool for learning. Similarly, when you are working on a team, take your colleagues' feedback positively and always try to improve.

Life-long Learning

Programming equals life-long learning. If you are interested in learning how to code, you must know that you will always need to be learning new things as new technologies emerge and existing technologies are updated. Think about it... that is great because there is always something interesting and new to learn!

Open to Trying New Things

Finally, an essential skill to be a successful programmer is to be open to trying new things. Step out of your comfort zone and be open to new technologies and products. In the technology industry, things evolve very quickly and adapting to change is essential.

🔸 Tips for Learning How to Code

Now that you know more about programming, programming languages, and the skills you need to be a successful programmer, let's see some tips for learning how to code.

💡 Tip: these tips are based on my personal experience and opinions.

• Choose one programming language to learn first. When you are learning how to code, it's easy to feel overwhelmed with the number of options and entry paths. My advice would be to focus on understanding the essential computer science concepts and one programming language first. Python and JavaScript are great options to start learning the fundamentals.
• Take detailed notes. Note-taking skills are essential to record and to analyze the topics you are learning. You can add custom comments and annotations to explain what you are learning.
• Practice constantly. You can only improve your problem-solving skills by practicing and by learning new techniques and tools. Try to practice every day.

💡 Tip: There is a challenge called the #100DaysOfCode challenge that you can join to practice every day.  

• Always try again. If you can't solve a problem on your first try, take a break and come back again and again until you solve it. That is the only way to learn. Learn from your mistakes and learn new approaches.
• Learn how to research and how to find answers. Programming languages, libraries, and frameworks usually have official documentations that explain their built-in elements and tools and how you can use them. This is a precious resource that you should definitely refer to.
• Browse Stack Overflow . This is an amazing platform. It is like an online encyclopedia of answers to common programming questions. You can find answers to existing questions and ask new questions to get help from the community.
• Set goals. Motivation is one of the most important factors for success. Setting goals is very important to keep you focused, motivated, and enthusiastic. Once you reach your goals, set new ones that you find challenging and exciting.
• Create projects. When you are learning how to code, applying your skills will help you to expand your knowledge and remember things better. Creating projects is the perfect way to practice and to create a portfolio that you can show to potential employers.

🔹 Basic Programming Concepts

Great. If reading this article has helped you confirm that you want to learn programming, let's take your first steps.

These are some basic programming concepts that you should know:

• Variable: a variable is a name that we assign to a value in a computer program. When we define a variable, we assign a value to a name and we allocate a space in memory to store that value. The value of a variable can be updated during the program.
• Constant: a constant is similar to a variable. It stores a value but it cannot be modified. Once you assign a value to a constant, you cannot change it during the entire program.
• Conditional: a conditional is a programming structure that lets developers choose what the computer should do based on a condition. If the condition is True, something will happen but if the condition is False, something different can happen.
• Loop: a loop is a programming structure that let us run a code block (a sequence of instructions) multiple times. They are super helpful to avoid code repetition and to implement more complex functionality.
• Function: a function helps us to avoid code repetition and to reuse our code. It is like a code block to which we assign a name but it also has some special characteristics. We can write the name of the function to run that sequence of instructions without writing them again.

💡 Tip: Functions can communicate with main programs and main programs can communicate with functions through parameters , arguments , and return statements.

• Class: a class is used as a blueprint to define the characteristics and functionality of a type of object. Just like we have objects in our real world, we can represent objects in our programs.
• Bug: a bug is an error in the logic or implementation of a program that results in an unexpected or incorrect output.
• Debugging: debugging is the process of finding and fixing bugs in a program.
• IDE: this acronym stands for Integrated Development Environment. It is a software development environment that has the most helpful tools that you will need to write computer programs such as a file editor, an explorer, a terminal, and helpful menu options.

💡 Tip: a commonly used and free IDE is Visual Studio Code , created by Microsoft.

Awesome! Now you know some of the fundamental concepts in programming. Like you learned, each programming language has a different syntax, but they all share most of these programming structures and concepts.  

🔸 Types of Programming Languages

Programming languages can be classified based on different criteria. If you want to learn how to code, it's important for you to learn these basic classifications:

• High-level programming languages: they are designed to be understood by humans and they have to be converted into machine code before the computer can understand them. They are the programming languages that we commonly use. For example: JavaScript, Python, Java, C#, C++, and Kotlin.
• Low-level programming languages: they are more difficult to understand because they are not designed for humans. They are designed to be understood and processed efficiently by machines.

Conversion into Machine Code

• Compiled programming languages: programs written with this type of programming language are converted directly into machine code by a compiler. Examples include C, C++, Haskell, and Go.
• Interpreted programming languages: programs written with this type of programming language rely on another program called the interpreter, which is in charge of running the code line by line. Examples include Python, JavaScript, PHP, and Ruby.

💡 Tip: according to this article on freeCodeCamp's publication:

Most programming languages can have both compiled and interpreted implementations – the language itself is not necessarily compiled or interpreted. However, for simplicity’s sake, they’re typically referred to as such.

There are other types of programming languages based on different criteria, such as:

• Procedural programming languages
• Functional programming languages
• Object-oriented programming languages
• Scripting languages
• Logic programming languages

And the list of types of programming languages continues. This is very interesting because you can analyze the characteristics of a programming language to help you choose the right one for your project.

🔹 How to Contribute to Open Source Projects

Finally, you might think that coding implies sitting at a desk for many hours looking at your code without any human interaction. But let me tell you that this does not have to be true at all. You can be part of a learning community or a developer community.

Initially, when you are learning how to code, you can participate in a learning community like freeCodeCamp. This way, you will share your journey with others who are learning how to code, just like you.

Then, when you have enough skills and confidence in your knowledge, you can practice by contributing to open source projects and join developer communities.

Open source software is defined by Opensource.com as:

Software with source code that anyone can inspect, modify, and enhance.

GitHub is an online platform for hosting projects with version control. There, you can find many open source projects (like freeCodeCamp ) that you can contribute to and practice your skills.

💡 Tip: many open source projects welcome first-time contributions and contributions from all skill levels. These are great opportunities to practice your skills and to contribute to real-world projects.  

Contributing to open source projects on GitHub is great to acquire new experience working and communicating with other developers. This is another important skill for finding a job in this field.

Working on a team is a great experience. I totally recommend it once you feel comfortable enough with your skills and knowledge.

You did it! You reached the end of this article. Great work. Now you know what programming is all about. Let's see a brief summary.

🔸 In Summary

• Programming is a very powerful skill. If you learn how to code, you can make your vision come true.
• Programming has many different applications in many different fields. You can find an application for programming in basically any field you choose.
• Programming languages can be classified based on different criteria and they share basic concepts such as variables, conditionals, loops, and functions.
• Always set goals and take detailed notes. To succeed as a programmer, you need to be enthusiastic and consistent.

Thank you very much for reading my article. I hope you liked it and found it helpful. Now you know why you should learn how to code.

🔅 I invite you to follow me on Twitter ( @EstefaniaCassN ) and YouTube ( Coding with Estefania ) to find coding tutorials.

Developer, technical writer, and content creator @freeCodeCamp. I run the freeCodeCamp.org Español YouTube channel.

If you read this far, thank the author to show them you care. Say Thanks

Learn to code for free. freeCodeCamp's open source curriculum has helped more than 40,000 people get jobs as developers. Get started

35 problem-solving techniques and methods for solving complex problems

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All teams and organizations encounter challenges as they grow. There are problems that might occur for teams when it comes to miscommunication or resolving business-critical issues . You may face challenges around growth , design , user engagement, and even team culture and happiness. In short, problem-solving techniques should be part of every team’s skillset.

Problem-solving methods are primarily designed to help a group or team through a process of first identifying problems and challenges , ideating possible solutions , and then evaluating the most suitable .

Finding effective solutions to complex problems isn’t easy, but by using the right process and techniques, you can help your team be more efficient in the process.

So how do you develop strategies that are engaging, and empower your team to solve problems effectively?

In this blog post, we share a series of problem-solving tools you can use in your next workshop or team meeting. You’ll also find some tips for facilitating the process and how to enable others to solve complex problems.

Let’s get started! 

How do you identify problems?

How do you identify the right solution.

• Tips for more effective problem-solving

Complete problem-solving methods

• Problem-solving techniques to identify and analyze problems
• Problem-solving techniques for developing solutions

Problem-solving warm-up activities

Closing activities for a problem-solving process.

Before you can move towards finding the right solution for a given problem, you first need to identify and define the problem you wish to solve. 

Here, you want to clearly articulate what the problem is and allow your group to do the same. Remember that everyone in a group is likely to have differing perspectives and alignment is necessary in order to help the group move forward. 

Identifying a problem accurately also requires that all members of a group are able to contribute their views in an open and safe manner. It can be scary for people to stand up and contribute, especially if the problems or challenges are emotive or personal in nature. Be sure to try and create a psychologically safe space for these kinds of discussions.

Remember that problem analysis and further discussion are also important. Not taking the time to fully analyze and discuss a challenge can result in the development of solutions that are not fit for purpose or do not address the underlying issue.

Successfully identifying and then analyzing a problem means facilitating a group through activities designed to help them clearly and honestly articulate their thoughts and produce usable insight.

With this data, you might then produce a problem statement that clearly describes the problem you wish to be addressed and also state the goal of any process you undertake to tackle this issue.  

Finding solutions is the end goal of any process. Complex organizational challenges can only be solved with an appropriate solution but discovering them requires using the right problem-solving tool.

After you’ve explored a problem and discussed ideas, you need to help a team discuss and choose the right solution. Consensus tools and methods such as those below help a group explore possible solutions before then voting for the best. They’re a great way to tap into the collective intelligence of the group for great results!

Remember that the process is often iterative. Great problem solvers often roadtest a viable solution in a measured way to see what works too. While you might not get the right solution on your first try, the methods below help teams land on the most likely to succeed solution while also holding space for improvement.

Every effective problem solving process begins with an agenda . A well-structured workshop is one of the best methods for successfully guiding a group from exploring a problem to implementing a solution.

In SessionLab, it’s easy to go from an idea to a complete agenda . Start by dragging and dropping your core problem solving activities into place . Add timings, breaks and necessary materials before sharing your agenda with your colleagues.

The resulting agenda will be your guide to an effective and productive problem solving session that will also help you stay organized on the day!

Tips for more effective problem solving

Problem-solving activities are only one part of the puzzle. While a great method can help unlock your team’s ability to solve problems, without a thoughtful approach and strong facilitation the solutions may not be fit for purpose.

Let’s take a look at some problem-solving tips you can apply to any process to help it be a success!

Clearly define the problem

Jumping straight to solutions can be tempting, though without first clearly articulating a problem, the solution might not be the right one. Many of the problem-solving activities below include sections where the problem is explored and clearly defined before moving on.

This is a vital part of the problem-solving process and taking the time to fully define an issue can save time and effort later. A clear definition helps identify irrelevant information and it also ensures that your team sets off on the right track.

Don’t jump to conclusions

It’s easy for groups to exhibit cognitive bias or have preconceived ideas about both problems and potential solutions. Be sure to back up any problem statements or potential solutions with facts, research, and adequate forethought.

The best techniques ask participants to be methodical and challenge preconceived notions. Make sure you give the group enough time and space to collect relevant information and consider the problem in a new way. By approaching the process with a clear, rational mindset, you’ll often find that better solutions are more forthcoming.  

Try different approaches  

Problems come in all shapes and sizes and so too should the methods you use to solve them. If you find that one approach isn’t yielding results and your team isn’t finding different solutions, try mixing it up. You’ll be surprised at how using a new creative activity can unblock your team and generate great solutions.

Don’t take it personally 

Depending on the nature of your team or organizational problems, it’s easy for conversations to get heated. While it’s good for participants to be engaged in the discussions, ensure that emotions don’t run too high and that blame isn’t thrown around while finding solutions.

You’re all in it together, and even if your team or area is seeing problems, that isn’t necessarily a disparagement of you personally. Using facilitation skills to manage group dynamics is one effective method of helping conversations be more constructive.

Get the right people in the room

Your problem-solving method is often only as effective as the group using it. Getting the right people on the job and managing the number of people present is important too!

If the group is too small, you may not get enough different perspectives to effectively solve a problem. If the group is too large, you can go round and round during the ideation stages.

Creating the right group makeup is also important in ensuring you have the necessary expertise and skillset to both identify and follow up on potential solutions. Carefully consider who to include at each stage to help ensure your problem-solving method is followed and positioned for success.

Document everything

The best solutions can take refinement, iteration, and reflection to come out. Get into a habit of documenting your process in order to keep all the learnings from the session and to allow ideas to mature and develop. Many of the methods below involve the creation of documents or shared resources. Be sure to keep and share these so everyone can benefit from the work done!

Bring a facilitator 

Facilitation is all about making group processes easier. With a subject as potentially emotive and important as problem-solving, having an impartial third party in the form of a facilitator can make all the difference in finding great solutions and keeping the process moving. Consider bringing a facilitator to your problem-solving session to get better results and generate meaningful solutions!

Develop your problem-solving skills

It takes time and practice to be an effective problem solver. While some roles or participants might more naturally gravitate towards problem-solving, it can take development and planning to help everyone create better solutions.

You might develop a training program, run a problem-solving workshop or simply ask your team to practice using the techniques below. Check out our post on problem-solving skills to see how you and your group can develop the right mental process and be more resilient to issues too!

Design a great agenda

Workshops are a great format for solving problems. With the right approach, you can focus a group and help them find the solutions to their own problems. But designing a process can be time-consuming and finding the right activities can be difficult.

Check out our workshop planning guide to level-up your agenda design and start running more effective workshops. Need inspiration? Check out templates designed by expert facilitators to help you kickstart your process!

In this section, we’ll look at in-depth problem-solving methods that provide a complete end-to-end process for developing effective solutions. These will help guide your team from the discovery and definition of a problem through to delivering the right solution.

If you’re looking for an all-encompassing method or problem-solving model, these processes are a great place to start. They’ll ask your team to challenge preconceived ideas and adopt a mindset for solving problems more effectively.

• Six Thinking Hats
• Lightning Decision Jam
• Problem Definition Process
• Discovery & Action Dialogue

Design Sprint 2.0

• Open Space Technology

1. Six Thinking Hats

Individual approaches to solving a problem can be very different based on what team or role an individual holds. It can be easy for existing biases or perspectives to find their way into the mix, or for internal politics to direct a conversation.

Six Thinking Hats is a classic method for identifying the problems that need to be solved and enables your team to consider them from different angles, whether that is by focusing on facts and data, creative solutions, or by considering why a particular solution might not work.

Like all problem-solving frameworks, Six Thinking Hats is effective at helping teams remove roadblocks from a conversation or discussion and come to terms with all the aspects necessary to solve complex problems.

2. Lightning Decision Jam

Featured courtesy of Jonathan Courtney of AJ&Smart Berlin, Lightning Decision Jam is one of those strategies that should be in every facilitation toolbox. Exploring problems and finding solutions is often creative in nature, though as with any creative process, there is the potential to lose focus and get lost.

Unstructured discussions might get you there in the end, but it’s much more effective to use a method that creates a clear process and team focus.

In Lightning Decision Jam, participants are invited to begin by writing challenges, concerns, or mistakes on post-its without discussing them before then being invited by the moderator to present them to the group.

From there, the team vote on which problems to solve and are guided through steps that will allow them to reframe those problems, create solutions and then decide what to execute on. 

By deciding the problems that need to be solved as a team before moving on, this group process is great for ensuring the whole team is aligned and can take ownership over the next stages. 

Lightning Decision Jam (LDJ)   #action   #decision making   #problem solving   #issue analysis   #innovation   #design   #remote-friendly   The problem with anything that requires creative thinking is that it’s easy to get lost—lose focus and fall into the trap of having useless, open-ended, unstructured discussions. Here’s the most effective solution I’ve found: Replace all open, unstructured discussion with a clear process. What to use this exercise for: Anything which requires a group of people to make decisions, solve problems or discuss challenges. It’s always good to frame an LDJ session with a broad topic, here are some examples: The conversion flow of our checkout Our internal design process How we organise events Keeping up with our competition Improving sales flow

3. Problem Definition Process

While problems can be complex, the problem-solving methods you use to identify and solve those problems can often be simple in design. 

By taking the time to truly identify and define a problem before asking the group to reframe the challenge as an opportunity, this method is a great way to enable change.

Begin by identifying a focus question and exploring the ways in which it manifests before splitting into five teams who will each consider the problem using a different method: escape, reversal, exaggeration, distortion or wishful. Teams develop a problem objective and create ideas in line with their method before then feeding them back to the group.

This method is great for enabling in-depth discussions while also creating space for finding creative solutions too!

Problem Definition   #problem solving   #idea generation   #creativity   #online   #remote-friendly   A problem solving technique to define a problem, challenge or opportunity and to generate ideas.

4. The 5 Whys 

Sometimes, a group needs to go further with their strategies and analyze the root cause at the heart of organizational issues. An RCA or root cause analysis is the process of identifying what is at the heart of business problems or recurring challenges. 

The 5 Whys is a simple and effective method of helping a group go find the root cause of any problem or challenge and conduct analysis that will deliver results. 

By beginning with the creation of a problem statement and going through five stages to refine it, The 5 Whys provides everything you need to truly discover the cause of an issue.

The 5 Whys   #hyperisland   #innovation   This simple and powerful method is useful for getting to the core of a problem or challenge. As the title suggests, the group defines a problems, then asks the question “why” five times, often using the resulting explanation as a starting point for creative problem solving.

5. World Cafe

World Cafe is a simple but powerful facilitation technique to help bigger groups to focus their energy and attention on solving complex problems.

World Cafe enables this approach by creating a relaxed atmosphere where participants are able to self-organize and explore topics relevant and important to them which are themed around a central problem-solving purpose. Create the right atmosphere by modeling your space after a cafe and after guiding the group through the method, let them take the lead!

Making problem-solving a part of your organization’s culture in the long term can be a difficult undertaking. More approachable formats like World Cafe can be especially effective in bringing people unfamiliar with workshops into the fold. 

World Cafe   #hyperisland   #innovation   #issue analysis   World Café is a simple yet powerful method, originated by Juanita Brown, for enabling meaningful conversations driven completely by participants and the topics that are relevant and important to them. Facilitators create a cafe-style space and provide simple guidelines. Participants then self-organize and explore a set of relevant topics or questions for conversation.

6. Discovery & Action Dialogue (DAD)

One of the best approaches is to create a safe space for a group to share and discover practices and behaviors that can help them find their own solutions.

With DAD, you can help a group choose which problems they wish to solve and which approaches they will take to do so. It’s great at helping remove resistance to change and can help get buy-in at every level too!

This process of enabling frontline ownership is great in ensuring follow-through and is one of the methods you will want in your toolbox as a facilitator.

Discovery & Action Dialogue (DAD)   #idea generation   #liberating structures   #action   #issue analysis   #remote-friendly   DADs make it easy for a group or community to discover practices and behaviors that enable some individuals (without access to special resources and facing the same constraints) to find better solutions than their peers to common problems. These are called positive deviant (PD) behaviors and practices. DADs make it possible for people in the group, unit, or community to discover by themselves these PD practices. DADs also create favorable conditions for stimulating participants’ creativity in spaces where they can feel safe to invent new and more effective practices. Resistance to change evaporates as participants are unleashed to choose freely which practices they will adopt or try and which problems they will tackle. DADs make it possible to achieve frontline ownership of solutions.

7. Design Sprint 2.0

Want to see how a team can solve big problems and move forward with prototyping and testing solutions in a few days? The Design Sprint 2.0 template from Jake Knapp, author of Sprint, is a complete agenda for a with proven results.

Developing the right agenda can involve difficult but necessary planning. Ensuring all the correct steps are followed can also be stressful or time-consuming depending on your level of experience.

Use this complete 4-day workshop template if you are finding there is no obvious solution to your challenge and want to focus your team around a specific problem that might require a shortcut to launching a minimum viable product or waiting for the organization-wide implementation of a solution.

8. Open space technology

Open space technology- developed by Harrison Owen – creates a space where large groups are invited to take ownership of their problem solving and lead individual sessions. Open space technology is a great format when you have a great deal of expertise and insight in the room and want to allow for different takes and approaches on a particular theme or problem you need to be solved.

Start by bringing your participants together to align around a central theme and focus their efforts. Explain the ground rules to help guide the problem-solving process and then invite members to identify any issue connecting to the central theme that they are interested in and are prepared to take responsibility for.

Once participants have decided on their approach to the core theme, they write their issue on a piece of paper, announce it to the group, pick a session time and place, and post the paper on the wall. As the wall fills up with sessions, the group is then invited to join the sessions that interest them the most and which they can contribute to, then you’re ready to begin!

Everyone joins the problem-solving group they’ve signed up to, record the discussion and if appropriate, findings can then be shared with the rest of the group afterward.

Open Space Technology   #action plan   #idea generation   #problem solving   #issue analysis   #large group   #online   #remote-friendly   Open Space is a methodology for large groups to create their agenda discerning important topics for discussion, suitable for conferences, community gatherings and whole system facilitation

Techniques to identify and analyze problems

Using a problem-solving method to help a team identify and analyze a problem can be a quick and effective addition to any workshop or meeting.

While further actions are always necessary, you can generate momentum and alignment easily, and these activities are a great place to get started.

We’ve put together this list of techniques to help you and your team with problem identification, analysis, and discussion that sets the foundation for developing effective solutions.

Let’s take a look!

• The Creativity Dice
• Fishbone Analysis
• Problem Tree
• SWOT Analysis
• Agreement-Certainty Matrix
• The Journalistic Six
• LEGO Challenge
• What, So What, Now What?
• Journalists

Individual and group perspectives are incredibly important, but what happens if people are set in their minds and need a change of perspective in order to approach a problem more effectively?

Flip It is a method we love because it is both simple to understand and run, and allows groups to understand how their perspectives and biases are formed. 

Participants in Flip It are first invited to consider concerns, issues, or problems from a perspective of fear and write them on a flip chart. Then, the group is asked to consider those same issues from a perspective of hope and flip their understanding.  

No problem and solution is free from existing bias and by changing perspectives with Flip It, you can then develop a problem solving model quickly and effectively.

Flip It!   #gamestorming   #problem solving   #action   Often, a change in a problem or situation comes simply from a change in our perspectives. Flip It! is a quick game designed to show players that perspectives are made, not born.

10. The Creativity Dice

One of the most useful problem solving skills you can teach your team is of approaching challenges with creativity, flexibility, and openness. Games like The Creativity Dice allow teams to overcome the potential hurdle of too much linear thinking and approach the process with a sense of fun and speed. 

In The Creativity Dice, participants are organized around a topic and roll a dice to determine what they will work on for a period of 3 minutes at a time. They might roll a 3 and work on investigating factual information on the chosen topic. They might roll a 1 and work on identifying the specific goals, standards, or criteria for the session.

Encouraging rapid work and iteration while asking participants to be flexible are great skills to cultivate. Having a stage for idea incubation in this game is also important. Moments of pause can help ensure the ideas that are put forward are the most suitable. 

The Creativity Dice   #creativity   #problem solving   #thiagi   #issue analysis   Too much linear thinking is hazardous to creative problem solving. To be creative, you should approach the problem (or the opportunity) from different points of view. You should leave a thought hanging in mid-air and move to another. This skipping around prevents premature closure and lets your brain incubate one line of thought while you consciously pursue another.

11. Fishbone Analysis

Organizational or team challenges are rarely simple, and it’s important to remember that one problem can be an indication of something that goes deeper and may require further consideration to be solved.

Fishbone Analysis helps groups to dig deeper and understand the origins of a problem. It’s a great example of a root cause analysis method that is simple for everyone on a team to get their head around. 

Participants in this activity are asked to annotate a diagram of a fish, first adding the problem or issue to be worked on at the head of a fish before then brainstorming the root causes of the problem and adding them as bones on the fish. 

Using abstractions such as a diagram of a fish can really help a team break out of their regular thinking and develop a creative approach.

Fishbone Analysis   #problem solving   ##root cause analysis   #decision making   #online facilitation   A process to help identify and understand the origins of problems, issues or observations.

12. Problem Tree 

Encouraging visual thinking can be an essential part of many strategies. By simply reframing and clarifying problems, a group can move towards developing a problem solving model that works for them. 

In Problem Tree, groups are asked to first brainstorm a list of problems – these can be design problems, team problems or larger business problems – and then organize them into a hierarchy. The hierarchy could be from most important to least important or abstract to practical, though the key thing with problem solving games that involve this aspect is that your group has some way of managing and sorting all the issues that are raised.

Once you have a list of problems that need to be solved and have organized them accordingly, you’re then well-positioned for the next problem solving steps.

Problem tree   #define intentions   #create   #design   #issue analysis   A problem tree is a tool to clarify the hierarchy of problems addressed by the team within a design project; it represents high level problems or related sublevel problems.

13. SWOT Analysis

Chances are you’ve heard of the SWOT Analysis before. This problem-solving method focuses on identifying strengths, weaknesses, opportunities, and threats is a tried and tested method for both individuals and teams.

Start by creating a desired end state or outcome and bare this in mind – any process solving model is made more effective by knowing what you are moving towards. Create a quadrant made up of the four categories of a SWOT analysis and ask participants to generate ideas based on each of those quadrants.

Once you have those ideas assembled in their quadrants, cluster them together based on their affinity with other ideas. These clusters are then used to facilitate group conversations and move things forward. 

SWOT analysis   #gamestorming   #problem solving   #action   #meeting facilitation   The SWOT Analysis is a long-standing technique of looking at what we have, with respect to the desired end state, as well as what we could improve on. It gives us an opportunity to gauge approaching opportunities and dangers, and assess the seriousness of the conditions that affect our future. When we understand those conditions, we can influence what comes next.

14. Agreement-Certainty Matrix

Not every problem-solving approach is right for every challenge, and deciding on the right method for the challenge at hand is a key part of being an effective team.

The Agreement Certainty matrix helps teams align on the nature of the challenges facing them. By sorting problems from simple to chaotic, your team can understand what methods are suitable for each problem and what they can do to ensure effective results. 

If you are already using Liberating Structures techniques as part of your problem-solving strategy, the Agreement-Certainty Matrix can be an invaluable addition to your process. We’ve found it particularly if you are having issues with recurring problems in your organization and want to go deeper in understanding the root cause. 

Agreement-Certainty Matrix   #issue analysis   #liberating structures   #problem solving   You can help individuals or groups avoid the frequent mistake of trying to solve a problem with methods that are not adapted to the nature of their challenge. The combination of two questions makes it possible to easily sort challenges into four categories: simple, complicated, complex , and chaotic .  A problem is simple when it can be solved reliably with practices that are easy to duplicate.  It is complicated when experts are required to devise a sophisticated solution that will yield the desired results predictably.  A problem is complex when there are several valid ways to proceed but outcomes are not predictable in detail.  Chaotic is when the context is too turbulent to identify a path forward.  A loose analogy may be used to describe these differences: simple is like following a recipe, complicated like sending a rocket to the moon, complex like raising a child, and chaotic is like the game “Pin the Tail on the Donkey.”  The Liberating Structures Matching Matrix in Chapter 5 can be used as the first step to clarify the nature of a challenge and avoid the mismatches between problems and solutions that are frequently at the root of chronic, recurring problems.

Organizing and charting a team’s progress can be important in ensuring its success. SQUID (Sequential Question and Insight Diagram) is a great model that allows a team to effectively switch between giving questions and answers and develop the skills they need to stay on track throughout the process. 

Begin with two different colored sticky notes – one for questions and one for answers – and with your central topic (the head of the squid) on the board. Ask the group to first come up with a series of questions connected to their best guess of how to approach the topic. Ask the group to come up with answers to those questions, fix them to the board and connect them with a line. After some discussion, go back to question mode by responding to the generated answers or other points on the board.

It’s rewarding to see a diagram grow throughout the exercise, and a completed SQUID can provide a visual resource for future effort and as an example for other teams.

SQUID   #gamestorming   #project planning   #issue analysis   #problem solving   When exploring an information space, it’s important for a group to know where they are at any given time. By using SQUID, a group charts out the territory as they go and can navigate accordingly. SQUID stands for Sequential Question and Insight Diagram.

16. Speed Boat

To continue with our nautical theme, Speed Boat is a short and sweet activity that can help a team quickly identify what employees, clients or service users might have a problem with and analyze what might be standing in the way of achieving a solution.

Methods that allow for a group to make observations, have insights and obtain those eureka moments quickly are invaluable when trying to solve complex problems.

In Speed Boat, the approach is to first consider what anchors and challenges might be holding an organization (or boat) back. Bonus points if you are able to identify any sharks in the water and develop ideas that can also deal with competitors!   

Speed Boat   #gamestorming   #problem solving   #action   Speedboat is a short and sweet way to identify what your employees or clients don’t like about your product/service or what’s standing in the way of a desired goal.

17. The Journalistic Six

Some of the most effective ways of solving problems is by encouraging teams to be more inclusive and diverse in their thinking.

Based on the six key questions journalism students are taught to answer in articles and news stories, The Journalistic Six helps create teams to see the whole picture. By using who, what, when, where, why, and how to facilitate the conversation and encourage creative thinking, your team can make sure that the problem identification and problem analysis stages of the are covered exhaustively and thoughtfully. Reporter’s notebook and dictaphone optional.

The Journalistic Six – Who What When Where Why How   #idea generation   #issue analysis   #problem solving   #online   #creative thinking   #remote-friendly   A questioning method for generating, explaining, investigating ideas.

18. LEGO Challenge

Now for an activity that is a little out of the (toy) box. LEGO Serious Play is a facilitation methodology that can be used to improve creative thinking and problem-solving skills. 

The LEGO Challenge includes giving each member of the team an assignment that is hidden from the rest of the group while they create a structure without speaking.

What the LEGO challenge brings to the table is a fun working example of working with stakeholders who might not be on the same page to solve problems. Also, it’s LEGO! Who doesn’t love LEGO! 

LEGO Challenge   #hyperisland   #team   A team-building activity in which groups must work together to build a structure out of LEGO, but each individual has a secret “assignment” which makes the collaborative process more challenging. It emphasizes group communication, leadership dynamics, conflict, cooperation, patience and problem solving strategy.

19. What, So What, Now What?

If not carefully managed, the problem identification and problem analysis stages of the problem-solving process can actually create more problems and misunderstandings.

The What, So What, Now What? problem-solving activity is designed to help collect insights and move forward while also eliminating the possibility of disagreement when it comes to identifying, clarifying, and analyzing organizational or work problems. 

Facilitation is all about bringing groups together so that might work on a shared goal and the best problem-solving strategies ensure that teams are aligned in purpose, if not initially in opinion or insight.

Throughout the three steps of this game, you give everyone on a team to reflect on a problem by asking what happened, why it is important, and what actions should then be taken. 

This can be a great activity for bringing our individual perceptions about a problem or challenge and contextualizing it in a larger group setting. This is one of the most important problem-solving skills you can bring to your organization.

W³ – What, So What, Now What?   #issue analysis   #innovation   #liberating structures   You can help groups reflect on a shared experience in a way that builds understanding and spurs coordinated action while avoiding unproductive conflict. It is possible for every voice to be heard while simultaneously sifting for insights and shaping new direction. Progressing in stages makes this practical—from collecting facts about What Happened to making sense of these facts with So What and finally to what actions logically follow with Now What . The shared progression eliminates most of the misunderstandings that otherwise fuel disagreements about what to do. Voila!

20. Journalists  

Problem analysis can be one of the most important and decisive stages of all problem-solving tools. Sometimes, a team can become bogged down in the details and are unable to move forward.

Journalists is an activity that can avoid a group from getting stuck in the problem identification or problem analysis stages of the process.

In Journalists, the group is invited to draft the front page of a fictional newspaper and figure out what stories deserve to be on the cover and what headlines those stories will have. By reframing how your problems and challenges are approached, you can help a team move productively through the process and be better prepared for the steps to follow.

Journalists   #vision   #big picture   #issue analysis   #remote-friendly   This is an exercise to use when the group gets stuck in details and struggles to see the big picture. Also good for defining a vision.

Problem-solving techniques for developing solutions 

The success of any problem-solving process can be measured by the solutions it produces. After you’ve defined the issue, explored existing ideas, and ideated, it’s time to narrow down to the correct solution.

Use these problem-solving techniques when you want to help your team find consensus, compare possible solutions, and move towards taking action on a particular problem.

• Improved Solutions
• Four-Step Sketch
• 15% Solutions
• How-Now-Wow matrix
• Impact Effort Matrix

21. Mindspin  

Brainstorming is part of the bread and butter of the problem-solving process and all problem-solving strategies benefit from getting ideas out and challenging a team to generate solutions quickly. 

With Mindspin, participants are encouraged not only to generate ideas but to do so under time constraints and by slamming down cards and passing them on. By doing multiple rounds, your team can begin with a free generation of possible solutions before moving on to developing those solutions and encouraging further ideation. 

This is one of our favorite problem-solving activities and can be great for keeping the energy up throughout the workshop. Remember the importance of helping people become engaged in the process – energizing problem-solving techniques like Mindspin can help ensure your team stays engaged and happy, even when the problems they’re coming together to solve are complex. 

MindSpin   #teampedia   #idea generation   #problem solving   #action   A fast and loud method to enhance brainstorming within a team. Since this activity has more than round ideas that are repetitive can be ruled out leaving more creative and innovative answers to the challenge.

22. Improved Solutions

After a team has successfully identified a problem and come up with a few solutions, it can be tempting to call the work of the problem-solving process complete. That said, the first solution is not necessarily the best, and by including a further review and reflection activity into your problem-solving model, you can ensure your group reaches the best possible result. 

One of a number of problem-solving games from Thiagi Group, Improved Solutions helps you go the extra mile and develop suggested solutions with close consideration and peer review. By supporting the discussion of several problems at once and by shifting team roles throughout, this problem-solving technique is a dynamic way of finding the best solution. 

Improved Solutions   #creativity   #thiagi   #problem solving   #action   #team   You can improve any solution by objectively reviewing its strengths and weaknesses and making suitable adjustments. In this creativity framegame, you improve the solutions to several problems. To maintain objective detachment, you deal with a different problem during each of six rounds and assume different roles (problem owner, consultant, basher, booster, enhancer, and evaluator) during each round. At the conclusion of the activity, each player ends up with two solutions to her problem.

23. Four Step Sketch

Creative thinking and visual ideation does not need to be confined to the opening stages of your problem-solving strategies. Exercises that include sketching and prototyping on paper can be effective at the solution finding and development stage of the process, and can be great for keeping a team engaged. 

By going from simple notes to a crazy 8s round that involves rapidly sketching 8 variations on their ideas before then producing a final solution sketch, the group is able to iterate quickly and visually. Problem-solving techniques like Four-Step Sketch are great if you have a group of different thinkers and want to change things up from a more textual or discussion-based approach.

Four-Step Sketch   #design sprint   #innovation   #idea generation   #remote-friendly   The four-step sketch is an exercise that helps people to create well-formed concepts through a structured process that includes: Review key information Start design work on paper,  Consider multiple variations , Create a detailed solution . This exercise is preceded by a set of other activities allowing the group to clarify the challenge they want to solve. See how the Four Step Sketch exercise fits into a Design Sprint

24. 15% Solutions

Some problems are simpler than others and with the right problem-solving activities, you can empower people to take immediate actions that can help create organizational change. 

Part of the liberating structures toolkit, 15% solutions is a problem-solving technique that focuses on finding and implementing solutions quickly. A process of iterating and making small changes quickly can help generate momentum and an appetite for solving complex problems.

Problem-solving strategies can live and die on whether people are onboard. Getting some quick wins is a great way of getting people behind the process.   

It can be extremely empowering for a team to realize that problem-solving techniques can be deployed quickly and easily and delineate between things they can positively impact and those things they cannot change. 

15% Solutions   #action   #liberating structures   #remote-friendly   You can reveal the actions, however small, that everyone can do immediately. At a minimum, these will create momentum, and that may make a BIG difference.  15% Solutions show that there is no reason to wait around, feel powerless, or fearful. They help people pick it up a level. They get individuals and the group to focus on what is within their discretion instead of what they cannot change.  With a very simple question, you can flip the conversation to what can be done and find solutions to big problems that are often distributed widely in places not known in advance. Shifting a few grains of sand may trigger a landslide and change the whole landscape.

25. How-Now-Wow Matrix

The problem-solving process is often creative, as complex problems usually require a change of thinking and creative response in order to find the best solutions. While it’s common for the first stages to encourage creative thinking, groups can often gravitate to familiar solutions when it comes to the end of the process. 

When selecting solutions, you don’t want to lose your creative energy! The How-Now-Wow Matrix from Gamestorming is a great problem-solving activity that enables a group to stay creative and think out of the box when it comes to selecting the right solution for a given problem.

Problem-solving techniques that encourage creative thinking and the ideation and selection of new solutions can be the most effective in organisational change. Give the How-Now-Wow Matrix a go, and not just for how pleasant it is to say out loud. 

How-Now-Wow Matrix   #gamestorming   #idea generation   #remote-friendly   When people want to develop new ideas, they most often think out of the box in the brainstorming or divergent phase. However, when it comes to convergence, people often end up picking ideas that are most familiar to them. This is called a ‘creative paradox’ or a ‘creadox’. The How-Now-Wow matrix is an idea selection tool that breaks the creadox by forcing people to weigh each idea on 2 parameters.

26. Impact and Effort Matrix

All problem-solving techniques hope to not only find solutions to a given problem or challenge but to find the best solution. When it comes to finding a solution, groups are invited to put on their decision-making hats and really think about how a proposed idea would work in practice. 

The Impact and Effort Matrix is one of the problem-solving techniques that fall into this camp, empowering participants to first generate ideas and then categorize them into a 2×2 matrix based on impact and effort.

Activities that invite critical thinking while remaining simple are invaluable. Use the Impact and Effort Matrix to move from ideation and towards evaluating potential solutions before then committing to them. 

Impact and Effort Matrix   #gamestorming   #decision making   #action   #remote-friendly   In this decision-making exercise, possible actions are mapped based on two factors: effort required to implement and potential impact. Categorizing ideas along these lines is a useful technique in decision making, as it obliges contributors to balance and evaluate suggested actions before committing to them.

27. Dotmocracy

If you’ve followed each of the problem-solving steps with your group successfully, you should move towards the end of your process with heaps of possible solutions developed with a specific problem in mind. But how do you help a group go from ideation to putting a solution into action? 

Dotmocracy – or Dot Voting -is a tried and tested method of helping a team in the problem-solving process make decisions and put actions in place with a degree of oversight and consensus. 

One of the problem-solving techniques that should be in every facilitator’s toolbox, Dot Voting is fast and effective and can help identify the most popular and best solutions and help bring a group to a decision effectively. 

Dotmocracy   #action   #decision making   #group prioritization   #hyperisland   #remote-friendly   Dotmocracy is a simple method for group prioritization or decision-making. It is not an activity on its own, but a method to use in processes where prioritization or decision-making is the aim. The method supports a group to quickly see which options are most popular or relevant. The options or ideas are written on post-its and stuck up on a wall for the whole group to see. Each person votes for the options they think are the strongest, and that information is used to inform a decision.

All facilitators know that warm-ups and icebreakers are useful for any workshop or group process. Problem-solving workshops are no different.

Use these problem-solving techniques to warm up a group and prepare them for the rest of the process. Activating your group by tapping into some of the top problem-solving skills can be one of the best ways to see great outcomes from your session.

• Check-in/Check-out
• Doodling Together
• Show and Tell
• Constellations
• Draw a Tree

28. Check-in / Check-out

Solid processes are planned from beginning to end, and the best facilitators know that setting the tone and establishing a safe, open environment can be integral to a successful problem-solving process.

Check-in / Check-out is a great way to begin and/or bookend a problem-solving workshop. Checking in to a session emphasizes that everyone will be seen, heard, and expected to contribute. 

If you are running a series of meetings, setting a consistent pattern of checking in and checking out can really help your team get into a groove. We recommend this opening-closing activity for small to medium-sized groups though it can work with large groups if they’re disciplined!

Check-in / Check-out   #team   #opening   #closing   #hyperisland   #remote-friendly   Either checking-in or checking-out is a simple way for a team to open or close a process, symbolically and in a collaborative way. Checking-in/out invites each member in a group to be present, seen and heard, and to express a reflection or a feeling. Checking-in emphasizes presence, focus and group commitment; checking-out emphasizes reflection and symbolic closure.

29. Doodling Together  

Thinking creatively and not being afraid to make suggestions are important problem-solving skills for any group or team, and warming up by encouraging these behaviors is a great way to start. 

Doodling Together is one of our favorite creative ice breaker games – it’s quick, effective, and fun and can make all following problem-solving steps easier by encouraging a group to collaborate visually. By passing cards and adding additional items as they go, the workshop group gets into a groove of co-creation and idea development that is crucial to finding solutions to problems. 

Doodling Together   #collaboration   #creativity   #teamwork   #fun   #team   #visual methods   #energiser   #icebreaker   #remote-friendly   Create wild, weird and often funny postcards together & establish a group’s creative confidence.

30. Show and Tell

You might remember some version of Show and Tell from being a kid in school and it’s a great problem-solving activity to kick off a session.

Asking participants to prepare a little something before a workshop by bringing an object for show and tell can help them warm up before the session has even begun! Games that include a physical object can also help encourage early engagement before moving onto more big-picture thinking.

By asking your participants to tell stories about why they chose to bring a particular item to the group, you can help teams see things from new perspectives and see both differences and similarities in the way they approach a topic. Great groundwork for approaching a problem-solving process as a team! 

Show and Tell   #gamestorming   #action   #opening   #meeting facilitation   Show and Tell taps into the power of metaphors to reveal players’ underlying assumptions and associations around a topic The aim of the game is to get a deeper understanding of stakeholders’ perspectives on anything—a new project, an organizational restructuring, a shift in the company’s vision or team dynamic.

31. Constellations

Who doesn’t love stars? Constellations is a great warm-up activity for any workshop as it gets people up off their feet, energized, and ready to engage in new ways with established topics. It’s also great for showing existing beliefs, biases, and patterns that can come into play as part of your session.

Using warm-up games that help build trust and connection while also allowing for non-verbal responses can be great for easing people into the problem-solving process and encouraging engagement from everyone in the group. Constellations is great in large spaces that allow for movement and is definitely a practical exercise to allow the group to see patterns that are otherwise invisible. 

Constellations   #trust   #connection   #opening   #coaching   #patterns   #system   Individuals express their response to a statement or idea by standing closer or further from a central object. Used with teams to reveal system, hidden patterns, perspectives.

32. Draw a Tree

Problem-solving games that help raise group awareness through a central, unifying metaphor can be effective ways to warm-up a group in any problem-solving model.

Draw a Tree is a simple warm-up activity you can use in any group and which can provide a quick jolt of energy. Start by asking your participants to draw a tree in just 45 seconds – they can choose whether it will be abstract or realistic. 

Once the timer is up, ask the group how many people included the roots of the tree and use this as a means to discuss how we can ignore important parts of any system simply because they are not visible.

All problem-solving strategies are made more effective by thinking of problems critically and by exposing things that may not normally come to light. Warm-up games like Draw a Tree are great in that they quickly demonstrate some key problem-solving skills in an accessible and effective way.

Draw a Tree   #thiagi   #opening   #perspectives   #remote-friendly   With this game you can raise awarness about being more mindful, and aware of the environment we live in.

Each step of the problem-solving workshop benefits from an intelligent deployment of activities, games, and techniques. Bringing your session to an effective close helps ensure that solutions are followed through on and that you also celebrate what has been achieved.

Here are some problem-solving activities you can use to effectively close a workshop or meeting and ensure the great work you’ve done can continue afterward.

• One Breath Feedback
• Who What When Matrix
• Response Cards

How do I conclude a problem-solving process?

All good things must come to an end. With the bulk of the work done, it can be tempting to conclude your workshop swiftly and without a moment to debrief and align. This can be problematic in that it doesn’t allow your team to fully process the results or reflect on the process.

At the end of an effective session, your team will have gone through a process that, while productive, can be exhausting. It’s important to give your group a moment to take a breath, ensure that they are clear on future actions, and provide short feedback before leaving the space. 

The primary purpose of any problem-solving method is to generate solutions and then implement them. Be sure to take the opportunity to ensure everyone is aligned and ready to effectively implement the solutions you produced in the workshop.

Remember that every process can be improved and by giving a short moment to collect feedback in the session, you can further refine your problem-solving methods and see further success in the future too.

33. One Breath Feedback

Maintaining attention and focus during the closing stages of a problem-solving workshop can be tricky and so being concise when giving feedback can be important. It’s easy to incur “death by feedback” should some team members go on for too long sharing their perspectives in a quick feedback round. 

One Breath Feedback is a great closing activity for workshops. You give everyone an opportunity to provide feedback on what they’ve done but only in the space of a single breath. This keeps feedback short and to the point and means that everyone is encouraged to provide the most important piece of feedback to them. 

One breath feedback   #closing   #feedback   #action   This is a feedback round in just one breath that excels in maintaining attention: each participants is able to speak during just one breath … for most people that’s around 20 to 25 seconds … unless of course you’ve been a deep sea diver in which case you’ll be able to do it for longer.

34. Who What When Matrix 

Matrices feature as part of many effective problem-solving strategies and with good reason. They are easily recognizable, simple to use, and generate results.

The Who What When Matrix is a great tool to use when closing your problem-solving session by attributing a who, what and when to the actions and solutions you have decided upon. The resulting matrix is a simple, easy-to-follow way of ensuring your team can move forward. 

Great solutions can’t be enacted without action and ownership. Your problem-solving process should include a stage for allocating tasks to individuals or teams and creating a realistic timeframe for those solutions to be implemented or checked out. Use this method to keep the solution implementation process clear and simple for all involved. 

Who/What/When Matrix   #gamestorming   #action   #project planning   With Who/What/When matrix, you can connect people with clear actions they have defined and have committed to.

35. Response cards

Group discussion can comprise the bulk of most problem-solving activities and by the end of the process, you might find that your team is talked out! 

Providing a means for your team to give feedback with short written notes can ensure everyone is head and can contribute without the need to stand up and talk. Depending on the needs of the group, giving an alternative can help ensure everyone can contribute to your problem-solving model in the way that makes the most sense for them.

Response Cards is a great way to close a workshop if you are looking for a gentle warm-down and want to get some swift discussion around some of the feedback that is raised. 

Response Cards   #debriefing   #closing   #structured sharing   #questions and answers   #thiagi   #action   It can be hard to involve everyone during a closing of a session. Some might stay in the background or get unheard because of louder participants. However, with the use of Response Cards, everyone will be involved in providing feedback or clarify questions at the end of a session.

Save time and effort discovering the right solutions

A structured problem solving process is a surefire way of solving tough problems, discovering creative solutions and driving organizational change. But how can you design for successful outcomes?

With SessionLab, it’s easy to design engaging workshops that deliver results. Drag, drop and reorder blocks  to build your agenda. When you make changes or update your agenda, your session  timing   adjusts automatically , saving you time on manual adjustments.

Collaborating with stakeholders or clients? Share your agenda with a single click and collaborate in real-time. No more sending documents back and forth over email.

Explore  how to use SessionLab  to design effective problem solving workshops or  watch this five minute video  to see the planner in action!

Over to you

The problem-solving process can often be as complicated and multifaceted as the problems they are set-up to solve. With the right problem-solving techniques and a mix of creative exercises designed to guide discussion and generate purposeful ideas, we hope we’ve given you the tools to find the best solutions as simply and easily as possible.

Is there a problem-solving technique that you are missing here? Do you have a favorite activity or method you use when facilitating? Let us know in the comments below, we’d love to hear from you! 

thank you very much for these excellent techniques

Certainly wonderful article, very detailed. Shared!

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• 1. Micro-Worlds
• 2. Light-Bot in Java
• 3. Jeroos of Santong Island
• 4. Problem Solving and Algorithms
• 5. Creating Jeroo Methods
• 6. Conditionally Executing Actions
• 7. Repeating Actions
• 8. Handling Touch Events
• 9. Adding Text to the Screen

Problem Solving and Algorithms

Learn a basic process for developing a solution to a problem. Nothing in this chapter is unique to using a computer to solve a problem. This process can be used to solve a wide variety of problems, including ones that have nothing to do with computers.

Problems, Solutions, and Tools

I have a problem! I need to thank Aunt Kay for the birthday present she sent me. I could send a thank you note through the mail. I could call her on the telephone. I could send her an email message. I could drive to her house and thank her in person. In fact, there are many ways I could thank her, but that's not the point. The point is that I must decide how I want to solve the problem, and use the appropriate tool to implement (carry out) my plan. The postal service, the telephone, the internet, and my automobile are tools that I can use, but none of these actually solves my problem. In a similar way, a computer does not solve problems, it's just a tool that I can use to implement my plan for solving the problem.

Knowing that Aunt Kay appreciates creative and unusual things, I have decided to hire a singing messenger to deliver my thanks. In this context, the messenger is a tool, but one that needs instructions from me. I have to tell the messenger where Aunt Kay lives, what time I would like the message to be delivered, and what lyrics I want sung. A computer program is similar to my instructions to the messenger.

The story of Aunt Kay uses a familiar context to set the stage for a useful point of view concerning computers and computer programs. The following list summarizes the key aspects of this point of view.

A computer is a tool that can be used to implement a plan for solving a problem.

A computer program is a set of instructions for a computer. These instructions describe the steps that the computer must follow to implement a plan.

An algorithm is a plan for solving a problem.

A person must design an algorithm.

A person must translate an algorithm into a computer program.

This point of view sets the stage for a process that we will use to develop solutions to Jeroo problems. The basic process is important because it can be used to solve a wide variety of problems, including ones where the solution will be written in some other programming language.

An Algorithm Development Process

Every problem solution starts with a plan. That plan is called an algorithm.

There are many ways to write an algorithm. Some are very informal, some are quite formal and mathematical in nature, and some are quite graphical. The instructions for connecting a DVD player to a television are an algorithm. A mathematical formula such as πR 2 is a special case of an algorithm. The form is not particularly important as long as it provides a good way to describe and check the logic of the plan.

The development of an algorithm (a plan) is a key step in solving a problem. Once we have an algorithm, we can translate it into a computer program in some programming language. Our algorithm development process consists of five major steps.

Step 1: Obtain a description of the problem.

Step 2: analyze the problem., step 3: develop a high-level algorithm., step 4: refine the algorithm by adding more detail., step 5: review the algorithm..

This step is much more difficult than it appears. In the following discussion, the word client refers to someone who wants to find a solution to a problem, and the word developer refers to someone who finds a way to solve the problem. The developer must create an algorithm that will solve the client's problem.

The client is responsible for creating a description of the problem, but this is often the weakest part of the process. It's quite common for a problem description to suffer from one or more of the following types of defects: (1) the description relies on unstated assumptions, (2) the description is ambiguous, (3) the description is incomplete, or (4) the description has internal contradictions. These defects are seldom due to carelessness by the client. Instead, they are due to the fact that natural languages (English, French, Korean, etc.) are rather imprecise. Part of the developer's responsibility is to identify defects in the description of a problem, and to work with the client to remedy those defects.

The purpose of this step is to determine both the starting and ending points for solving the problem. This process is analogous to a mathematician determining what is given and what must be proven. A good problem description makes it easier to perform this step.

When determining the starting point, we should start by seeking answers to the following questions:

What data are available?

Where is that data?

What formulas pertain to the problem?

What rules exist for working with the data?

What relationships exist among the data values?

When determining the ending point, we need to describe the characteristics of a solution. In other words, how will we know when we're done? Asking the following questions often helps to determine the ending point.

What new facts will we have?

What items will have changed?

What changes will have been made to those items?

What things will no longer exist?

An algorithm is a plan for solving a problem, but plans come in several levels of detail. It's usually better to start with a high-level algorithm that includes the major part of a solution, but leaves the details until later. We can use an everyday example to demonstrate a high-level algorithm.

Problem: I need a send a birthday card to my brother, Mark.

Analysis: I don't have a card. I prefer to buy a card rather than make one myself.

High-level algorithm:

Go to a store that sells greeting cards Select a card Purchase a card Mail the card

This algorithm is satisfactory for daily use, but it lacks details that would have to be added were a computer to carry out the solution. These details include answers to questions such as the following.

"Which store will I visit?"

"How will I get there: walk, drive, ride my bicycle, take the bus?"

"What kind of card does Mark like: humorous, sentimental, risqué?"

These kinds of details are considered in the next step of our process.

A high-level algorithm shows the major steps that need to be followed to solve a problem. Now we need to add details to these steps, but how much detail should we add? Unfortunately, the answer to this question depends on the situation. We have to consider who (or what) is going to implement the algorithm and how much that person (or thing) already knows how to do. If someone is going to purchase Mark's birthday card on my behalf, my instructions have to be adapted to whether or not that person is familiar with the stores in the community and how well the purchaser known my brother's taste in greeting cards.

When our goal is to develop algorithms that will lead to computer programs, we need to consider the capabilities of the computer and provide enough detail so that someone else could use our algorithm to write a computer program that follows the steps in our algorithm. As with the birthday card problem, we need to adjust the level of detail to match the ability of the programmer. When in doubt, or when you are learning, it is better to have too much detail than to have too little.

Most of our examples will move from a high-level to a detailed algorithm in a single step, but this is not always reasonable. For larger, more complex problems, it is common to go through this process several times, developing intermediate level algorithms as we go. Each time, we add more detail to the previous algorithm, stopping when we see no benefit to further refinement. This technique of gradually working from a high-level to a detailed algorithm is often called stepwise refinement .

The final step is to review the algorithm. What are we looking for? First, we need to work through the algorithm step by step to determine whether or not it will solve the original problem. Once we are satisfied that the algorithm does provide a solution to the problem, we start to look for other things. The following questions are typical of ones that should be asked whenever we review an algorithm. Asking these questions and seeking their answers is a good way to develop skills that can be applied to the next problem.

Does this algorithm solve a very specific problem or does it solve a more general problem ? If it solves a very specific problem, should it be generalized?

For example, an algorithm that computes the area of a circle having radius 5.2 meters (formula π*5.2 2 ) solves a very specific problem, but an algorithm that computes the area of any circle (formula π*R 2 ) solves a more general problem.

Can this algorithm be simplified ?

One formula for computing the perimeter of a rectangle is:

length + width + length + width

A simpler formula would be:

2.0 * ( length + width )

Is this solution similar to the solution to another problem? How are they alike? How are they different?

For example, consider the following two formulae:

Rectangle area = length * width Triangle area = 0.5 * base * height

Similarities: Each computes an area. Each multiplies two measurements.

Differences: Different measurements are used. The triangle formula contains 0.5.

Hypothesis: Perhaps every area formula involves multiplying two measurements.

Example 4.1: Pick and Plant

This section contains an extended example that demonstrates the algorithm development process. To complete the algorithm, we need to know that every Jeroo can hop forward, turn left and right, pick a flower from its current location, and plant a flower at its current location.

Problem Statement (Step 1)

A Jeroo starts at (0, 0) facing East with no flowers in its pouch. There is a flower at location (3, 0). Write a program that directs the Jeroo to pick the flower and plant it at location (3, 2). After planting the flower, the Jeroo should hop one space East and stop. There are no other nets, flowers, or Jeroos on the island.

Analysis of the Problem (Step 2)

The flower is exactly three spaces ahead of the jeroo.

The flower is to be planted exactly two spaces South of its current location.

The Jeroo is to finish facing East one space East of the planted flower.

There are no nets to worry about.

High-level Algorithm (Step 3)

Let's name the Jeroo Bobby. Bobby should do the following:

Get the flower Put the flower Hop East

Detailed Algorithm (Step 4)

Get the flower Hop 3 times Pick the flower Put the flower Turn right Hop 2 times Plant a flower Hop East Turn left Hop once

Review the Algorithm (Step 5)

The high-level algorithm partitioned the problem into three rather easy subproblems. This seems like a good technique.

This algorithm solves a very specific problem because the Jeroo and the flower are in very specific locations.

This algorithm is actually a solution to a slightly more general problem in which the Jeroo starts anywhere, and the flower is 3 spaces directly ahead of the Jeroo.

Java Code for "Pick and Plant"

A good programmer doesn't write a program all at once. Instead, the programmer will write and test the program in a series of builds. Each build adds to the previous one. The high-level algorithm will guide us in this process.

FIRST BUILD

To see this solution in action, create a new Greenfoot4Sofia scenario and use the Edit Palettes Jeroo menu command to make the Jeroo classes visible. Right-click on the Island class and create a new subclass with the name of your choice. This subclass will hold your new code.

The recommended first build contains three things:

The main method (here myProgram() in your island subclass).

Declaration and instantiation of every Jeroo that will be used.

The high-level algorithm in the form of comments.

The instantiation at the beginning of myProgram() places bobby at (0, 0), facing East, with no flowers.

Once the first build is working correctly, we can proceed to the others. In this case, each build will correspond to one step in the high-level algorithm. It may seem like a lot of work to use four builds for such a simple program, but doing so helps establish habits that will become invaluable as the programs become more complex.

SECOND BUILD

This build adds the logic to "get the flower", which in the detailed algorithm (step 4 above) consists of hopping 3 times and then picking the flower. The new code is indicated by comments that wouldn't appear in the original (they are just here to call attention to the additions). The blank lines help show the organization of the logic.

By taking a moment to run the work so far, you can confirm whether or not this step in the planned algorithm works as expected.

THIRD BUILD

This build adds the logic to "put the flower". New code is indicated by the comments that are provided here to mark the additions.

FOURTH BUILD (final)

Example 4.2: replace net with flower.

This section contains a second example that demonstrates the algorithm development process.

There are two Jeroos. One Jeroo starts at (0, 0) facing North with one flower in its pouch. The second starts at (0, 2) facing East with one flower in its pouch. There is a net at location (3, 2). Write a program that directs the first Jeroo to give its flower to the second one. After receiving the flower, the second Jeroo must disable the net, and plant a flower in its place. After planting the flower, the Jeroo must turn and face South. There are no other nets, flowers, or Jeroos on the island.

Jeroo_2 is exactly two spaces behind Jeroo_1.

The only net is exactly three spaces ahead of Jeroo_2.

Each Jeroo has exactly one flower.

Jeroo_2 will have two flowers after receiving one from Jeroo_1. One flower must be used to disable the net. The other flower must be planted at the location of the net, i.e. (3, 2).

Jeroo_1 will finish at (0, 1) facing South.

Jeroo_2 is to finish at (3, 2) facing South.

Each Jeroo will finish with 0 flowers in its pouch. One flower was used to disable the net, and the other was planted.

Let's name the first Jeroo Ann and the second one Andy.

Ann should do the following: Find Andy (but don't collide with him) Give a flower to Andy (he will be straight ahead) After receiving the flower, Andy should do the following: Find the net (but don't hop onto it) Disable the net Plant a flower at the location of the net Face South

Ann should do the following: Find Andy Turn around (either left or right twice) Hop (to location (0, 1)) Give a flower to Andy Give ahead Now Andy should do the following: Find the net Hop twice (to location (2, 2)) Disable the net Toss Plant a flower at the location of the net Hop (to location (3, 2)) Plant a flower Face South Turn right

The high-level algorithm helps manage the details.

This algorithm solves a very specific problem, but the specific locations are not important. The only thing that is important is the starting location of the Jeroos relative to one another and the location of the net relative to the second Jeroo's location and direction.

Java Code for "Replace Net with Flower"

As before, the code should be written incrementally as a series of builds. Four builds will be suitable for this problem. As usual, the first build will contain the main method, the declaration and instantiation of the Jeroo objects, and the high-level algorithm in the form of comments. The second build will have Ann give her flower to Andy. The third build will have Andy locate and disable the net. In the final build, Andy will place the flower and turn East.

This build creates the main method, instantiates the Jeroos, and outlines the high-level algorithm. In this example, the main method would be myProgram() contained within a subclass of Island .

This build adds the logic for Ann to locate Andy and give him a flower.

This build adds the logic for Andy to locate and disable the net.

This build adds the logic for Andy to place a flower at (3, 2) and turn South.

• The Art of Effective Problem Solving: A Step-by-Step Guide
• Learn Lean Sigma
• Problem Solving

Whether we realise it or not, problem solving skills are an important part of our daily lives. From resolving a minor annoyance at home to tackling complex business challenges at work, our ability to solve problems has a significant impact on our success and happiness. However, not everyone is naturally gifted at problem-solving, and even those who are can always improve their skills. In this blog post, we will go over the art of effective problem-solving step by step.

You will learn how to define a problem, gather information, assess alternatives, and implement a solution, all while honing your critical thinking and creative problem-solving skills. Whether you’re a seasoned problem solver or just getting started, this guide will arm you with the knowledge and tools you need to face any challenge with confidence. So let’s get started!

Table of Contents

Problem solving methodologies.

Individuals and organisations can use a variety of problem-solving methodologies to address complex challenges. 8D and A3 problem solving techniques are two popular methodologies in the Lean Six Sigma framework.

Methodology of 8D (Eight Discipline) Problem Solving:

The 8D problem solving methodology is a systematic, team-based approach to problem solving. It is a method that guides a team through eight distinct steps to solve a problem in a systematic and comprehensive manner.

The 8D process consists of the following steps:

• Form a team: Assemble a group of people who have the necessary expertise to work on the problem.
• Define the issue: Clearly identify and define the problem, including the root cause and the customer impact.
• Create a temporary containment plan: Put in place a plan to lessen the impact of the problem until a permanent solution can be found.
• Identify the root cause: To identify the underlying causes of the problem, use root cause analysis techniques such as Fishbone diagrams and Pareto charts.
• Create and test long-term corrective actions: Create and test a long-term solution to eliminate the root cause of the problem.
• Implement and validate the permanent solution: Implement and validate the permanent solution’s effectiveness.
• Prevent recurrence: Put in place measures to keep the problem from recurring.
• Recognize and reward the team: Recognize and reward the team for its efforts.

Download the 8D Problem Solving Template

A3 Problem Solving Method:

The A3 problem solving technique is a visual, team-based problem-solving approach that is frequently used in Lean Six Sigma projects. The A3 report is a one-page document that clearly and concisely outlines the problem, root cause analysis, and proposed solution.

The A3 problem-solving procedure consists of the following steps:

• Determine the issue: Define the issue clearly, including its impact on the customer.
• Perform root cause analysis: Identify the underlying causes of the problem using root cause analysis techniques.
• Create and implement a solution: Create and implement a solution that addresses the problem’s root cause.
• Monitor and improve the solution: Keep an eye on the solution’s effectiveness and make any necessary changes.

Subsequently, in the Lean Six Sigma framework, the 8D and A3 problem solving methodologies are two popular approaches to problem solving. Both methodologies provide a structured, team-based problem-solving approach that guides individuals through a comprehensive and systematic process of identifying, analysing, and resolving problems in an effective and efficient manner.

Step 1 – Define the Problem

The definition of the problem is the first step in effective problem solving. This may appear to be a simple task, but it is actually quite difficult. This is because problems are frequently complex and multi-layered, making it easy to confuse symptoms with the underlying cause. To avoid this pitfall, it is critical to thoroughly understand the problem.

To begin, ask yourself some clarifying questions:

• What exactly is the issue?
• What are the problem’s symptoms or consequences?
• Who or what is impacted by the issue?
• When and where does the issue arise?

Answering these questions will assist you in determining the scope of the problem. However, simply describing the problem is not always sufficient; you must also identify the root cause. The root cause is the underlying cause of the problem and is usually the key to resolving it permanently.

Try asking “why” questions to find the root cause:

• What causes the problem?
• Why does it continue?
• Why does it have the effects that it does?

By repeatedly asking “ why ,” you’ll eventually get to the bottom of the problem. This is an important step in the problem-solving process because it ensures that you’re dealing with the root cause rather than just the symptoms.

Once you have a firm grasp on the issue, it is time to divide it into smaller, more manageable chunks. This makes tackling the problem easier and reduces the risk of becoming overwhelmed. For example, if you’re attempting to solve a complex business problem, you might divide it into smaller components like market research, product development, and sales strategies.

To summarise step 1, defining the problem is an important first step in effective problem-solving. You will be able to identify the root cause and break it down into manageable parts if you take the time to thoroughly understand the problem. This will prepare you for the next step in the problem-solving process, which is gathering information and brainstorming ideas.

Step 2 – Gather Information and Brainstorm Ideas

Gathering information and brainstorming ideas is the next step in effective problem solving. This entails researching the problem and relevant information, collaborating with others, and coming up with a variety of potential solutions. This increases your chances of finding the best solution to the problem.

Begin by researching the problem and relevant information. This could include reading articles, conducting surveys, or consulting with experts. The goal is to collect as much information as possible in order to better understand the problem and possible solutions.

Next, work with others to gather a variety of perspectives. Brainstorming with others can be an excellent way to come up with new and creative ideas. Encourage everyone to share their thoughts and ideas when working in a group, and make an effort to actively listen to what others have to say. Be open to new and unconventional ideas and resist the urge to dismiss them too quickly.

Finally, use brainstorming to generate a wide range of potential solutions. This is the place where you can let your imagination run wild. At this stage, don’t worry about the feasibility or practicality of the solutions; instead, focus on generating as many ideas as possible. Write down everything that comes to mind, no matter how ridiculous or unusual it may appear. This can be done individually or in groups.

Once you’ve compiled a list of potential solutions, it’s time to assess them and select the best one. This is the next step in the problem-solving process, which we’ll go over in greater detail in the following section.

Step 3 – Evaluate Options and Choose the Best Solution

Once you’ve compiled a list of potential solutions, it’s time to assess them and select the best one. This is the third step in effective problem solving, and it entails weighing the advantages and disadvantages of each solution, considering their feasibility and practicability, and selecting the solution that is most likely to solve the problem effectively.

To begin, weigh the advantages and disadvantages of each solution. This will assist you in determining the potential outcomes of each solution and deciding which is the best option. For example, a quick and easy solution may not be the most effective in the long run, whereas a more complex and time-consuming solution may be more effective in solving the problem in the long run.

Consider each solution’s feasibility and practicability. Consider the following:

• Can the solution be implemented within the available resources, time, and budget?
• What are the possible barriers to implementing the solution?
• Is the solution feasible in today’s political, economic, and social environment?

You’ll be able to tell which solutions are likely to succeed and which aren’t by assessing their feasibility and practicability.

Finally, choose the solution that is most likely to effectively solve the problem. This solution should be based on the criteria you’ve established, such as the advantages and disadvantages of each solution, their feasibility and practicability, and your overall goals.

It is critical to remember that there is no one-size-fits-all solution to problems. What is effective for one person or situation may not be effective for another. This is why it is critical to consider a wide range of solutions and evaluate each one based on its ability to effectively solve the problem.

Step 4 – Implement and Monitor the Solution

When you’ve decided on the best solution, it’s time to put it into action. The fourth and final step in effective problem solving is to put the solution into action, monitor its progress, and make any necessary adjustments.

To begin, implement the solution. This may entail delegating tasks, developing a strategy, and allocating resources. Ascertain that everyone involved understands their role and responsibilities in the solution’s implementation.

Next, keep an eye on the solution’s progress. This may entail scheduling regular check-ins, tracking metrics, and soliciting feedback from others. You will be able to identify any potential roadblocks and make any necessary adjustments in a timely manner if you monitor the progress of the solution.

Finally, make any necessary modifications to the solution. This could entail changing the solution, altering the plan of action, or delegating different tasks. Be willing to make changes if they will improve the solution or help it solve the problem more effectively.

It’s important to remember that problem solving is an iterative process, and there may be times when you need to start from scratch. This is especially true if the initial solution does not effectively solve the problem. In these situations, it’s critical to be adaptable and flexible and to keep trying new solutions until you find the one that works best.

To summarise, effective problem solving is a critical skill that can assist individuals and organisations in overcoming challenges and achieving their objectives. Effective problem solving consists of four key steps: defining the problem, generating potential solutions, evaluating alternatives and selecting the best solution, and implementing the solution.

You can increase your chances of success in problem solving by following these steps and considering factors such as the pros and cons of each solution, their feasibility and practicability, and making any necessary adjustments. Furthermore, keep in mind that problem solving is an iterative process, and there may be times when you need to go back to the beginning and restart. Maintain your adaptability and try new solutions until you find the one that works best for you.

• Novick, L.R. and Bassok, M., 2005.  Problem Solving . Cambridge University Press.

Daniel Croft

Daniel Croft is a seasoned continuous improvement manager with a Black Belt in Lean Six Sigma. With over 10 years of real-world application experience across diverse sectors, Daniel has a passion for optimizing processes and fostering a culture of efficiency. He's not just a practitioner but also an avid learner, constantly seeking to expand his knowledge. Outside of his professional life, Daniel has a keen Investing, statistics and knowledge-sharing, which led him to create the website learnleansigma.com, a platform dedicated to Lean Six Sigma and process improvement insights.

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CBSE Class 11 | Problem Solving Methodologies

Problem solving process.

The process of problem-solving is an activity which has its ingredients as the specification of the program and the served dish is a correct program. This activity comprises of four steps : 1. Understanding the problem: To solve any problem it is very crucial to understand the problem first. What is the desired output of the code and how that output can be generated? The obvious and essential need to generate the output is an input. The input may be singular or it may be a set of inputs. A proper relationship between the input and output must be drawn in order to solve the problem efficiently. The input set should be complete and sufficient enough to draw the output. It means all the necessary inputs required to compute the output should be present at the time of computation. However, it should be kept in mind that the programmer should ensure that the minimum number of inputs should be there. Any irrelevant input only increases the size of and memory overhead of the program. Thus Identifying the minimum number of inputs required for output is a crucial element for understanding the problem.

2. Devising the plan: Once a problem has been understood, a proper action plan has to be devised to solve it. This is called devising the plan. This step usually involves computing the result from the given set of inputs. It uses the relationship drawn between inputs and outputs in the previous step. The complexity of this step depends upon the complexity of the problem at hand.

3. Executing the plan: Once the plan has been defined, it should follow the trajectory of action while ensuring the plan’s integrity at various checkpoints. If any inconsistency is found in between, the plan needs to be revised.

4. Evaluation: The final result so obtained must be evaluated and verified to see if the problem has been solved satisfactorily.

Problem Solving Methodology(The solution for the problem)

The methodology to solve a problem is defined as the most efficient solution to the problem. Although, there can be multiple ways to crack a nut, but a methodology is one where the nut is cracked in the shortest time and with minimum effort. Clearly, a sledgehammer can never be used to crack a nut. Under problem-solving methodology, we will see a step by step solution for a problem. These steps closely resemble the software life cycle . A software life cycle involves several stages in a program’s life cycle. These steps can be used by any tyro programmer to solve a problem in the most efficient way ever. The several steps of this cycle are as follows :

Step by step solution for a problem (Software Life Cycle) 1. Problem Definition/Specification: A computer program is basically a machine language solution to a real-life problem. Because programs are generally made to solve the pragmatic problems of the outside world. In order to solve the problem, it is very necessary to define the problem to get its proper understanding. For example, suppose we are asked to write a code for “ Compute the average of three numbers”. In this case, a proper definition of the problem will include questions like : “What exactly does average mean?” “How to calculate the average?”

Once, questions like these are raised, it helps to formulate the solution of the problem in a better way. Once a problem has been defined, the program’s specifications are then listed. Problem specifications describe what the program for the problem must do. It should definitely include :

what is the input set of the program

What is the desired output of the program and in what form the output is desired?

2. Problem Analysis (Breaking down the solution into simple steps): This step of solving the problem follows a modular approach to crack the nut. The problem is divided into subproblems so that designing a solution to these subproblems gets easier. The solutions to all these individual parts are then merged to get the final solution of the original problem. It is like divide and merge approach.

Modular Approach for Programming :

The process of breaking a large problem into subproblems and then treating these individual parts as different functions is called modular programming. Each function behaves independent of another and there is minimal inter-functional communication. There are two methods to implement modular programming :

• Top Down Design : In this method, the original problem is divided into subparts. These subparts are further divided. The chain continues till we get the very fundamental subpart of the problem which can’t be further divided. Then we draw a solution for each of these fundamental parts.
• Bottom Up Design : In this style of programming, an application is written by using the pre-existing primitives of programming language. These primitives are then amalgamated with more complicated features, till the application is written. This style is just the reverse of the top-down design style.

3. Problem Designing: The design of a problem can be represented in either of the two forms :

The ways to execute any program are of three categories:

• Sequence Statements Here, all the instructions are executed in a sequence, that is, one after the another, till the program is executed.
• Selection Statements As it is self-clear from the name, in these type of statements the whole set of instructions is not executed. A selection has to be made. A selected number of instructions are executed based on some condition. If the condition holds true then some part of the instruction set is executed, otherwise, another part of the set is executed. Since this selection out of the instruction set has to be made, thus these type of instructions are called Selection Statements.

Identification of arithmetic and logical operations required for the solution : While writing the algorithm for a problem, the arithmetic and logical operations required for the solution are also usually identified. They help to write the code in an easier manner because the proper ordering of the arithmetic and logical symbols is necessary to determine the correct output. And when all this has been done in the algorithm writing step, it just makes the coding task a smoother one.

• Flow Chart : Flow charts are diagrammatic representation of the algorithm. It uses some symbols to illustrate the starting and ending of a program along with the flow of instructions involved in the program.

4. Coding: Once an algorithm is formed, it can’t be executed on the computer. Thus in this step, this algorithm has to be translated into the syntax of a particular programming language. This process is often termed as ‘coding’. Coding is one of the most important steps of the software life cycle. It is not only challenging to find a solution to a problem but to write optimized code for a solution is far more challenging.

Writing code for optimizing execution time and memory storage : A programmer writes code on his local computer. Now, suppose he writes a code which takes 5 hours to get executed. Now, this 5 hours of time is actually the idle time for the programmer. Not only it takes longer time, but it also uses the resources during that time. One of the most precious computing resources is memory. A large program is expected to utilize more memory. However, memory utilization is not a fault, but if a program is utilizing unnecessary time or memory, then it is a fault of coding. The optimized code can save both time and memory. For example, as has been discussed earlier, by using the minimum number of inputs to compute the output , one can save unnecessary memory utilization. All such techniques are very necessary to be deployed to write optimized code. The pragmatic world gives reverence not only to the solution of the problem but to the optimized solution. This art of writing the optimized code also called ‘competitive programming’.

5. Program Testing and Debugging: Program testing involves running each and every instruction of the code and check the validity of the output by a sample input. By testing a program one can also check if there’s an error in the program. If an error is detected, then program debugging is done. It is a process to locate the instruction which is causing an error in the program and then rectifying it. There are different types of error in a program : (i) Syntax Error Every programming language has its own set of rules and constructs which need to be followed to form a valid program in that particular language. If at any place in the entire code, this set of rule is violated, it results in a syntax error. Take an example in C Language

In the above program, the syntax error is in the first printf statement since the printf statement doesn’t end with a ‘;’. Now, until and unless this error is not rectified, the program will not get executed.

Once the error is rectified, one gets the desired output. Suppose the input is ‘good’ then the output is : Output:

(ii) Logical Error An error caused due to the implementation of a wrong logic in the program is called logical error. They are usually detected during the runtime. Take an example in C Language:

In the above code, the ‘for’ loop won’t get executed since n has been initialized with the value of 11 while ‘for’ loop can only print values smaller than or equal to 10. Such a code will result in incorrect output and thus errors like these are called logical errors. Once the error is rectified, one gets the desired output. Suppose n is initialised with the value ‘5’ then the output is : Output:

(iii) Runtime Error Any error which causes the unusual termination of the program is called runtime error. They are detected at the run time. Some common examples of runtime errors are : Example 1 :

If during the runtime, the user gives the input value for B as 0 then the program terminates abruptly resulting in a runtime error. The output thus appears is : Output:

Example 2 : If while executing a program, one attempts for opening an unexisting file, that is, a file which is not present in the hard disk, it also results in a runtime error.

6. Documentation : The program documentation involves :

• Problem Definition
• Problem Design
• Documentation of test perform
• History of program development

7. Program Maintenance: Once a program has been formed, to ensure its longevity, maintenance is a must. The maintenance of a program has its own costs associated with it, which may also exceed the development cost of the program in some cases. The maintenance of a program involves the following :

• Detection and Elimination of undetected errors in the existing program.
• Modification of current program to enhance its performance and adaptability.
• Enhancement of user interface
• Enriching the program with new capabilities.
• Updation of the documentation.

Control Structure- Conditional control and looping (finite and infinite)

There are codes which usually involve looping statements. Looping statements are statements in which instruction or a set of instructions is executed multiple times until a particular condition is satisfied. The while loop, for loop, do while loop, etc. form the basis of such looping structure. These statements are also called control structure because they determine or control the flow of instructions in a program. These looping structures are of two kinds :

In the above program, the ‘for’ loop gets executed only until the value of i is less than or equal to 10. As soon as the value of i becomes greater than 10, the while loop is terminated. Output:

In the above code, one can easily see that the value of n is not getting incremented. In such a case, the value of n will always remain 1 and hence the while loop will never get executed. Such loop is called an infinite loop. Output:

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