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Top 10 Software Engineer Research Topics for 2024

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Software engineering, in general, is a dynamic and rapidly changing field that demands a thorough understanding of concepts related to programming, computer science, and mathematics. As software systems become more complicated in the future, software developers must stay updated on industry innovations and the latest trends. Working on software engineering research topics is an important part of staying relevant in the field of software engineering. 

Software engineers can do research to learn about new technologies, approaches, and strategies for developing and maintaining complex software systems. Software engineers can conduct research on a wide range of topics. Software engineering research is also vital for increasing the functionality, security, and dependability of software systems. Going for the Top Programming Certification course contributes to the advancement of the field's state of the art and assures that software engineers can continue to build high-quality, effective software systems.

What are Software Engineer Research Topics?

Software engineer research topics are areas of exploration and study in the rapidly evolving field of software engineering. These research topics include various software development approaches, quality of software, testing of software, maintenance of software, security measures for software, machine learning models in software engineering, DevOps, and architecture of software. Each of these software engineer research topics has distinct problems and opportunities for software engineers to investigate and make major contributions to the field. In short, research topics for software engineering provide possibilities for software engineers to investigate new technologies, approaches, and strategies for developing and managing complex software systems. 

For example, research on agile software development could identify the benefits and drawbacks of using agile methodology, as well as develop new techniques for effectively implementing agile practices. Software testing research may explore new testing procedures and tools, as well as assess the efficacy of existing ones. Software quality research may investigate the elements that influence software quality and develop approaches for enhancing software system quality and minimizing the faults and errors. Software metrics are quantitative measures that are used to assess the quality, maintainability, and performance of software. 

The research papers on software engineering topics in this specific area could identify novel measures for evaluating software systems or techniques for using metrics to improve the quality of software. The practice of integrating code changes into a common repository and pushing code changes to production in small, periodic batches is known as continuous integration and deployment (CI/CD). This research could investigate the best practices for establishing CI/CD or developing tools and approaches for automating the entire CI/CD process.

Top Software Engineer Research Topics

In this article we will be going through the following Software Engineer Research Topics:

1. Artificial Intelligence and Software Engineering

Intersections between AI and SE

The creation of AI-powered software engineering tools is one potential research area at the intersection of artificial intelligence (AI) and software engineering. These technologies use AI techniques that include machine learning, natural language processing, and computer vision to help software engineers with a variety of tasks throughout the software development lifecycle. An AI-powered code review tool, for example, may automatically discover potential flaws or security vulnerabilities in code, saving developers a lot of time and lowering the chance of human error. Similarly, an AI-powered testing tool might build test cases and analyze test results automatically to discover areas for improvement. 

Furthermore, AI-powered project management tools may aid in the planning and scheduling of projects, resource allocation, and risk management in the project. AI can also be utilized in software maintenance duties such as automatically discovering and correcting defects or providing code refactoring solutions. However, the development of such tools presents significant technical and ethical challenges, such as the necessity of large amounts of high-quality data, the risk of bias present in AI algorithms, and the possibility of AI replacing human jobs. Continuous study in this area is therefore required to ensure that AI-powered software engineering tools are successful, fair, and responsible.

Knowledge-based Software Engineering

Another study area that overlaps with AI and software engineering is knowledge-based software engineering (KBSE). KBSE entails creating software systems capable of reasoning about knowledge and applying that knowledge to enhance software development processes. The development of knowledge-based systems that can help software engineers in detecting and addressing complicated problems is one example of KBSE in action. To capture domain-specific knowledge, these systems use knowledge representation techniques such as ontologies, and reasoning algorithms such as logic programming or rule-based systems to derive new knowledge from already existing data. 

KBSE can be utilized in the context of AI and software engineering to create intelligent systems capable of learning from past experiences and applying that information to improvise future software development processes. A KBSE system, for example, may be used to generate code based on previous code samples or to recommend code snippets depending on the requirements of a project. Furthermore, KBSE systems could be used to improve the precision and efficiency of software testing and debugging by identifying and prioritizing bugs using knowledge-based techniques. As a result, continued research in this area is critical to ensuring that AI-powered software engineering tools are productive, fair, and responsible.

2. Natural Language Processing


Multimodality in Natural Language Processing (NLP) is one of the appealing research ideas for software engineering at the nexus of computer vision, speech recognition, and NLP. The ability of machines to comprehend and generate language from many modalities, such as text, speech, pictures, and video, is referred to as multimodal NLP. The goal of multimodal NLP is to develop systems that can learn from and interpret human communication across several modalities, allowing them to engage with humans in more organic and intuitive ways. 

The building of conversational agents or chatbots that can understand and create responses using several modalities is one example of multimodal NLP in action. These agents can analyze text input, voice input, and visual clues to provide more precise and relevant responses, allowing users to have a more natural and seamless conversational experience. Furthermore, multimodal NLP can be used to enhance language translation systems, allowing them to more accurately and effectively translate text, speech, and visual content.

The development of multimodal NLP systems must take efficiency into account. as multimodal NLP systems require significant computing power to process and integrate information from multiple modalities, optimizing their efficiency is critical to ensuring that they can operate in real-time and provide users with accurate and timely responses. Developing algorithms that can efficiently evaluate and integrate input from several modalities is one method for improving the efficiency of multimodal NLP systems. 

Overall, efficiency is a critical factor in the design of multimodal NLP systems. Researchers can increase the speed, precision, and scalability of these systems by inventing efficient algorithms, pre-processing approaches, and hardware architectures, allowing them to run successfully and offer real-time replies to consumers. Software Engineering training will help you level up your career and gear up to land you a job in the top product companies as a skilled Software Engineer. 

3. Applications of Data Mining in Software Engineering

Mining Software Engineering Data

The mining of software engineering data is one of the significant research paper topics for software engineering, involving the application of data mining techniques to extract insights from enormous datasets that are generated during software development processes. The purpose of mining software engineering data is to uncover patterns, trends, and various relationships that can inform software development practices, increase software product quality, and improve software development process efficiency. 

Mining software engineering data, despite its potential benefits, has various obstacles, including the quality of data, scalability, and privacy of data. Continuous research in this area is required to develop more effective data mining techniques and tools, as well as methods for ensuring data privacy and security, to address these challenges. By tackling these issues, mining software engineering data can continue to promote many positive aspects in software development practices and the overall quality of product.

Clustering and Text Mining

Clustering is a data mining approach that is used to group comparable items or data points based on their features or characteristics. Clustering can be used to detect patterns and correlations between different components of software, such as classes, methods, and modules, in the context of software engineering data. 

On the other hand, text mining is a method of data mining that is used to extract valuable information from unstructured text data such as software manuals, code comments, and bug reports. Text mining can be applied in the context of software engineering data to find patterns and trends in software development processes

4. Data Modeling

Data modeling is an important area of research paper topics in software engineering study, especially in the context of the design of databases and their management. It involves developing a conceptual model of the data that a system will need to store, organize, and manage, as well as establishing the relationships between various data pieces. One important goal of data modeling in software engineering research is to make sure that the database schema precisely matches the system's and its users' requirements. Working closely with stakeholders to understand their needs and identify the data items that are most essential to them is necessary.

5. Verification and Validation

Verification and validation are significant research project ideas for software engineering research because they help us to ensure that software systems are correctly built and suit the needs of their users. While most of the time, these terms are frequently used interchangeably, they refer to distinct stages of the software development process. The process of ensuring that a software system fits its specifications and needs is referred to as verification. This involves testing the system to confirm that it behaves as planned and satisfies the functional and performance specifications. In contrast, validation is the process of ensuring that a software system fulfils the needs of its users and stakeholders. 

This includes ensuring that the system serves its intended function and meets the requirements of its users. Verification and validation are key components of the software development process in software engineering research. Researchers can help to improve the functionality and dependability of software systems, minimize the chance of faults and mistakes, and ultimately develop better software products for their consumers by verifying that software systems are designed correctly and that they satisfy the needs of their users.

6. Software Project Management

Software project management is an important component of software engineering research because it comprises the planning, organization, and control of resources and activities to guarantee that software projects are finished on time, within budget, and to the needed quality standards. One of the key purposes of software project management in research is to guarantee that the project's stakeholders, such as users, clients, and sponsors, are satisfied with their needs. This includes defining the project's requirements, scope, and goals, as well as identifying potential risks and restrictions to the project's success.

7. Software Quality

The quality of a software product is defined as how well it fits in with its criteria, how well it performs its intended functions, and meets the needs of its consumers. It includes features such as dependability, usability, maintainability, effectiveness, and security, among others. Software quality is a prominent and essential research topic in software engineering. Researchers are working to provide methodologies, strategies, and tools for evaluating and improving software quality, as well as forecasting and preventing software faults and defects. Overall, software quality research is a large and interdisciplinary field that combines computer science, engineering, and statistics. Its mission is to increase the reliability, accessibility, and overall quality of software products and systems, thereby benefiting both software developers and end consumers.

8. Ontology

Ontology is a formal specification of a conception of a domain used in computer science to allow knowledge sharing and reuse. Ontology is a popular and essential area of study in the context of software engineering research. The construction of ontologies for specific domains or application areas could be a research topic in ontology for software engineering. For example, a researcher may create an ontology for the field of e-commerce to give common knowledge and terminology to software developers as well as stakeholders in that domain. The integration of several ontologies is another intriguing study topic in ontology for software engineering. As the number of ontologies generated for various domains and applications grows, there is an increasing need to integrate them in order to enable interoperability and reuse.

9. Software Models

In general, a software model acts as an abstract representation of a software system or its components. Software models can be used to help software developers, different stakeholders, and users communicate more effectively, as well as to properly evaluate, design, test, and maintain software systems. The development and evaluation of modeling languages and notations is one research example connected to software models. Researchers, for example, may evaluate the usefulness and efficiency of various modeling languages, such as UML or BPMN, for various software development activities or domains. 

Researchers could also look into using software models for software testing and verification. They may investigate how models might be used to produce test cases or to do model checking, a formal technique for ensuring the correctness of software systems. They may also examine the use of models for monitoring at runtime and software system adaptation.

The Software Development Life Cycle (SDLC) is a software engineering process for planning, designing, developing, testing, and deploying software systems. SDLC is an important research issue in software engineering since it is used to manage software projects and ensure the quality of the resultant software products by software developers and project managers. The development and evaluation of novel software development processes is one SDLC-related research topic. SDLC research also includes the creation and evaluation of different software project management tools and practices. 


Researchers may also check the implementation of SDLC in specific sectors or applications. They may, for example, investigate the use of SDLC in the development of systems that are more safety-critical, such as medical equipment or aviation systems, and develop new processes or tools to ensure the safety and reliability of these systems. They may also look into using SDLC to design software systems in new sectors like the Internet of Things or in blockchain technology.

Why is Software Engineering Required?

Software engineering is necessary because it gives a systematic way to developing, designing, and maintaining reliable, efficient, and scalable software. As software systems have become more complicated over time, software engineering has become a vital discipline to ensure that software is produced in a way that is fully compatible with end-user needs, reliable, and long-term maintainable.

When the cost of software development is considered, software engineering becomes even more important. Without a disciplined strategy, developing software can result in overinflated costs, delays, and a higher probability of errors that require costly adjustments later. Furthermore, software engineering can help reduce the long-term maintenance costs that occur by ensuring that software is designed to be easy to maintain and modify. This can save money in the long run by lowering the number of resources and time needed to make software changes as needed.

2. Scalability

Scalability is an essential factor in software development, especially for programs that have to manage enormous amounts of data or an increasing number of users. Software engineering provides a foundation for creating scalable software that can evolve over time. The capacity to deploy software to diverse contexts, such as cloud-based platforms or distributed systems, is another facet of scalability. Software engineering can assist in ensuring that software is built to be readily deployed and adjusted for various environments, resulting in increased flexibility and scalability.

3. Large Software

Developers can break down huge software systems into smaller, simpler parts using software engineering concepts, making the whole system easier to maintain. This can help to reduce the software's complexity and makes it easier to maintain the system over time. Furthermore, software engineering can aid in the development of large software systems in a modular fashion, with each module doing a specific function or set of functions. This makes it easier to push new features or functionality to the product without causing disruptions to the existing codebase.

4. Dynamic Nature

Developers can utilize software engineering techniques to create dynamic content that is modular and easily modifiable when user requirements change. This can enable adding new features or functionality to dynamic content easier without disturbing the existing codebase. Another factor to consider for dynamic content is security. Software engineering can assist in ensuring that dynamic content is generated in a secure manner that protects user data and information.

5. Better Quality Management

An organized method of quality management in software development is provided by software engineering. Developers may ensure that software is conceived, produced, and maintained in a way that fulfills quality requirements and provides value to users by adhering to software engineering principles. Requirement management is one component of quality management in software engineering. Testing and validation are another part of quality control in software engineering. Developers may verify that their software satisfies its requirements and is error-free by using an organized approach to testing.

In conclusion, the subject of software engineering provides a diverse set of research topics with the ability to progress the discipline while enhancing software development and maintenance procedures. This article has dived deep into various research topics in software engineering for masters and research topics for software engineering students such as software testing and validation, software security, artificial intelligence, Natural Language Processing, software project management, machine learning, Data Mining, etc. as research subjects. Software engineering researchers have an interesting chance to explore these and other research subjects and contribute to the development of creative solutions that can improve software quality, dependability, security, and scalability. 

Researchers may make important contributions to the area of software engineering and help tackle some of the most serious difficulties confronting software development and maintenance by staying updated with the latest research trends and technologies. As software grows more important in business and daily life, there is a greater demand for current research topics in software engineering into new software engineering processes and techniques. Software engineering researchers can assist in shaping the future of software creation and maintenance through their research, ensuring that software stays dependable, safe, reliable and efficient in an ever-changing technological context. KnowledgeHut’s top Programming certification course will help you leverage online programming courses from expert trainers.

Frequently Asked Questions (FAQs)

 To find a research topic in software engineering, you can review recent papers and conference proceedings, talk to different experts in the field, and evaluate your own interests and experience. You can use a combination of these approaches. 

You should study software development processes, various programming languages and their frameworks, software testing and quality assurance, software architecture, various design patterns that are currently being used, and software project management as a software engineering student. 

Empirical research, experimental research, surveys, case studies, and literature reviews are all types of research in software engineering. Each sort of study has advantages and disadvantages, and the research method chosen is determined by the research objective, resources, and available data. 


Eshaan Pandey

Eshaan is a Full Stack web developer skilled in MERN stack. He is a quick learner and has the ability to adapt quickly with respect to projects and technologies assigned to him. He has also worked previously on UI/UX web projects and delivered successfully. Eshaan has worked as an SDE Intern at Frazor for a span of 2 months. He has also worked as a Technical Blog Writer at KnowledgeHut upGrad writing articles on various technical topics.

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  • Design and Analysis of Computer Networks
  • Cyber-Physical Systems: Design, Analysis, and Security
  • The Impact of Nanotechnology on Computer Hardware
  • Wireless Sensor Networks: Design and Optimization
  • Cryptographic Hardware: Implementations and Security Evaluations
  • Machine Learning Techniques for Hardware Optimization
  • Hardware for Artificial Intelligence: GPUs vs. TPUs
  • Energy-Efficient Hardware Designs for Sustainable Computing
  • Security Aspects of Mobile and Ubiquitous Computing
  • Advanced Algorithms for Computer-Aided Design (CAD) of VLSI
  • Signal Processing in Communication Systems
  • The Development of Wearable Computing Devices
  • Computer Hardware Testing: Techniques and Tools
  • The Role of Hardware in Network Security
  • The Evolution of Interface Designs in Consumer Electronics
  • Biometric Systems: Hardware and Software Integration
  • The Integration of IoT Devices in Smart Environments
  • Electronic Design Automation (EDA) Tools and Methodologies
  • Robotics: Hardware Design and Control Systems
  • Hardware Accelerators for Deep Learning Applications
  • Developments in Non-Volatile Memory Technologies
  • The Future of Computer Hardware in the Era of Quantum Computing
  • Hardware Solutions for Data Storage and Retrieval
  • Power Management Techniques in Embedded Systems
  • Challenges in Designing Multi-Core Processors
  • System on Chip (SoC) Design Trends and Challenges
  • The Role of Computer Engineering in Aerospace Technology
  • Real-Time Systems: Design and Implementation Challenges
  • Hardware Support for Virtualization Technology
  • Advances in Computer Graphics Hardware
  • The Impact of 5G Technology on Mobile Computing Hardware
  • Environmental Impact Assessment of Computer Hardware Production
  • Security Vulnerabilities in Modern Microprocessors
  • Computer Hardware Innovations in the Automotive Industry
  • The Role of Computer Engineering in Medical Device Technology
  • Deep Learning Approaches to Object Recognition
  • Real-Time Image Processing for Autonomous Vehicles
  • Computer Vision in Robotic Surgery: Techniques and Challenges
  • Facial Recognition Technology: Innovations and Privacy Concerns
  • Machine Vision in Industrial Automation and Quality Control
  • 3D Reconstruction Techniques in Computer Vision
  • Enhancing Sports Analytics with Computer Vision
  • Augmented Reality: Integrating Computer Vision for Immersive Experiences
  • Computer Vision for Environmental Monitoring
  • Thermal Imaging and Its Applications in Computer Vision
  • Computer Vision in Retail: Customer Behavior and Store Layout Optimization
  • Motion Detection and Tracking in Security Systems
  • The Role of Computer Vision in Content Moderation on Social Media
  • Gesture Recognition: Methods and Applications
  • Computer Vision in Agriculture: Pest Detection and Crop Analysis
  • Advances in Medical Imaging: Machine Learning and Computer Vision
  • Scene Understanding and Contextual Inference in Images
  • The Development of Vision-Based Autonomous Drones
  • Optical Character Recognition (OCR): Latest Techniques and Applications
  • The Impact of Computer Vision on Virtual Reality Experiences
  • Biometrics: Enhancing Security Systems with Computer Vision
  • Computer Vision for Wildlife Conservation: Species Recognition and Behavior Analysis
  • Underwater Image Processing: Challenges and Techniques
  • Video Surveillance: The Evolution of Algorithmic Approaches
  • Advanced Driver-Assistance Systems (ADAS): Leveraging Computer Vision
  • Computational Photography: Enhancing Image Capture Techniques
  • The Integration of AI in Computer Vision: Ethical and Technical Considerations
  • Computer Vision in the Gaming Industry: From Design to Interaction
  • The Future of Computer Vision in Smart Cities
  • Pattern Recognition in Historical Document Analysis
  • The Role of Computer Vision in the Manufacturing of Customized Products
  • Enhancing Accessibility with Computer Vision: Tools for the Visually Impaired
  • The Use of Computer Vision in Behavioral Research
  • Predictive Analytics with Computer Vision in Sports
  • Image Synthesis with Generative Adversarial Networks (GANs)
  • The Use of Computer Vision in Remote Sensing
  • Real-Time Video Analytics for Public Safety
  • The Role of Computer Vision in Telemedicine
  • Computer Vision and the Internet of Things (IoT): A Synergistic Approach
  • Future Trends in Computer Vision: Quantum Computing and Beyond
  • Advances in Cryptography: Post-Quantum Cryptosystems
  • Artificial Intelligence in Cybersecurity: Threat Detection and Response
  • Blockchain for Enhanced Security in Distributed Networks
  • The Impact of IoT on Cybersecurity: Vulnerabilities and Solutions
  • Cybersecurity in Cloud Computing: Best Practices and Tools
  • Ethical Hacking: Techniques and Ethical Implications
  • The Role of Human Factors in Cybersecurity Breaches
  • Privacy-preserving Technologies in an Age of Surveillance
  • The Evolution of Ransomware Attacks and Defense Strategies
  • Secure Software Development: Integrating Security in DevOps (DevSecOps)
  • Cybersecurity in Critical Infrastructure: Challenges and Innovations
  • The Future of Biometric Security Systems
  • Cyber Warfare: State-sponsored Attacks and Defense Mechanisms
  • The Role of Cybersecurity in Protecting Digital Identities
  • Social Engineering Attacks: Prevention and Countermeasures
  • Mobile Security: Protecting Against Malware and Exploits
  • Wireless Network Security: Protocols and Practices
  • Data Breaches: Analysis, Consequences, and Mitigation
  • The Ethics of Cybersecurity: Balancing Privacy and Security
  • Regulatory Compliance and Cybersecurity: GDPR and Beyond
  • The Impact of 5G Technology on Cybersecurity
  • The Role of Machine Learning in Cyber Threat Intelligence
  • Cybersecurity in Automotive Systems: Challenges in a Connected Environment
  • The Use of Virtual Reality for Cybersecurity Training and Simulation
  • Advanced Persistent Threats (APT): Detection and Response
  • Cybersecurity for Smart Cities: Challenges and Solutions
  • Deep Learning Applications in Malware Detection
  • The Role of Cybersecurity in Healthcare: Protecting Patient Data
  • Supply Chain Cybersecurity: Identifying Risks and Solutions
  • Endpoint Security: Trends, Challenges, and Future Directions
  • Forensic Techniques in Cybersecurity: Tracking and Analyzing Cyber Crimes
  • The Influence of International Law on Cyber Operations
  • Protecting Financial Institutions from Cyber Frauds and Attacks
  • Quantum Computing and Its Implications for Cybersecurity
  • Cybersecurity and Remote Work: Emerging Threats and Strategies
  • IoT Security in Industrial Applications
  • Cyber Insurance: Risk Assessment and Management
  • Security Challenges in Edge Computing Environments
  • Anomaly Detection in Network Security Using AI Techniques
  • Securing the Software Supply Chain in Application Development
  • Big Data Analytics: Techniques and Applications in Real-time
  • Machine Learning Algorithms for Predictive Analytics
  • Data Science in Healthcare: Improving Patient Outcomes with Predictive Models
  • The Role of Data Science in Financial Market Predictions
  • Natural Language Processing: Emerging Trends and Applications
  • Data Visualization Tools and Techniques for Enhanced Business Intelligence
  • Ethics in Data Science: Privacy, Fairness, and Transparency
  • The Use of Data Science in Environmental Science for Sustainability Studies
  • The Impact of Data Science on Social Media Marketing Strategies
  • Data Mining Techniques for Detecting Patterns in Large Datasets
  • AI and Data Science: Synergies and Future Prospects
  • Reinforcement Learning: Applications and Challenges in Data Science
  • The Role of Data Science in E-commerce Personalization
  • Predictive Maintenance in Manufacturing Through Data Science
  • The Evolution of Recommendation Systems in Streaming Services
  • Real-time Data Processing with Stream Analytics
  • Deep Learning for Image and Video Analysis
  • Data Governance in Big Data Analytics
  • Text Analytics and Sentiment Analysis for Customer Feedback
  • Fraud Detection in Banking and Insurance Using Data Science
  • The Integration of IoT Data in Data Science Models
  • The Future of Data Science in Quantum Computing
  • Data Science for Public Health: Epidemic Outbreak Prediction
  • Sports Analytics: Performance Improvement and Injury Prevention
  • Data Science in Retail: Inventory Management and Customer Journey Analysis
  • Data Science in Smart Cities: Traffic and Urban Planning
  • The Use of Blockchain in Data Security and Integrity
  • Geospatial Analysis for Environmental Monitoring
  • Time Series Analysis in Economic Forecasting
  • Data Science in Education: Analyzing Trends and Student Performance
  • Predictive Policing: Data Science in Law Enforcement
  • Data Science in Agriculture: Yield Prediction and Soil Health
  • Computational Social Science: Analyzing Societal Trends
  • Data Science in Energy Sector: Consumption and Optimization
  • Personalization Technologies in Healthcare Through Data Science
  • The Role of Data Science in Content Creation and Media
  • Anomaly Detection in Network Security Using Data Science Techniques
  • The Future of Autonomous Vehicles: Data Science-Driven Innovations
  • Multimodal Data Fusion Techniques in Data Science
  • Scalability Challenges in Data Science Projects
  • The Role of Digital Transformation in Business Model Innovation
  • The Impact of Digital Technologies on Customer Experience
  • Digital Transformation in the Banking Sector: Trends and Challenges
  • The Use of AI and Robotics in Digital Transformation of Manufacturing
  • Digital Transformation in Healthcare: Telemedicine and Beyond
  • The Influence of Big Data on Decision-Making Processes in Corporations
  • Blockchain as a Driver for Transparency in Digital Transformation
  • The Role of IoT in Enhancing Operational Efficiency in Industries
  • Digital Marketing Strategies: SEO, Content, and Social Media
  • The Integration of Cyber-Physical Systems in Industrial Automation
  • Digital Transformation in Education: Virtual Learning Environments
  • Smart Cities: The Role of Digital Technologies in Urban Planning
  • Digital Transformation in the Retail Sector: E-commerce Evolution
  • The Future of Work: Impact of Digital Transformation on Workplaces
  • Cybersecurity Challenges in a Digitally Transformed World
  • Mobile Technologies and Their Impact on Digital Transformation
  • The Role of Digital Twin Technology in Industry 4.0
  • Digital Transformation in the Public Sector: E-Government Services
  • Data Privacy and Security in the Age of Digital Transformation
  • Digital Transformation in the Energy Sector: Smart Grids and Renewable Energy
  • The Use of Augmented Reality in Training and Development
  • The Role of Virtual Reality in Real Estate and Architecture
  • Digital Transformation and Sustainability: Reducing Environmental Footprint
  • The Role of Digital Transformation in Supply Chain Optimization
  • Digital Transformation in Agriculture: IoT and Smart Farming
  • The Impact of 5G on Digital Transformation Initiatives
  • The Influence of Digital Transformation on Media and Entertainment
  • Digital Transformation in Insurance: Telematics and Risk Assessment
  • The Role of AI in Enhancing Customer Service Operations
  • The Future of Digital Transformation: Trends and Predictions
  • Digital Transformation and Corporate Governance
  • The Role of Leadership in Driving Digital Transformation
  • Digital Transformation in Non-Profit Organizations: Challenges and Benefits
  • The Economic Implications of Digital Transformation
  • The Cultural Impact of Digital Transformation on Organizations
  • Digital Transformation in Transportation: Logistics and Fleet Management
  • User Experience (UX) Design in Digital Transformation
  • The Role of Digital Transformation in Crisis Management
  • Digital Transformation and Human Resource Management
  • Implementing Change Management in Digital Transformation Projects
  • Scalability Challenges in Distributed Systems: Solutions and Strategies
  • Blockchain Technology: Enhancing Security and Transparency in Distributed Networks
  • The Role of Edge Computing in Distributed Systems
  • Designing Fault-Tolerant Systems in Distributed Networks
  • The Impact of 5G Technology on Distributed Network Architectures
  • Machine Learning Algorithms for Network Traffic Analysis
  • Load Balancing Techniques in Distributed Computing
  • The Use of Distributed Ledger Technology Beyond Cryptocurrencies
  • Network Function Virtualization (NFV) and Its Impact on Service Providers
  • The Evolution of Software-Defined Networking (SDN) in Enterprise Environments
  • Implementing Robust Cybersecurity Measures in Distributed Systems
  • Quantum Computing: Implications for Network Security in Distributed Systems
  • Peer-to-Peer Network Protocols and Their Applications
  • The Internet of Things (IoT): Network Challenges and Communication Protocols
  • Real-Time Data Processing in Distributed Sensor Networks
  • The Role of Artificial Intelligence in Optimizing Network Operations
  • Privacy and Data Protection Strategies in Distributed Systems
  • The Future of Distributed Computing in Cloud Environments
  • Energy Efficiency in Distributed Network Systems
  • Wireless Mesh Networks: Design, Challenges, and Applications
  • Multi-Access Edge Computing (MEC): Use Cases and Deployment Challenges
  • Consensus Algorithms in Distributed Systems: From Blockchain to New Applications
  • The Use of Containers and Microservices in Building Scalable Applications
  • Network Slicing for 5G: Opportunities and Challenges
  • The Role of Distributed Systems in Big Data Analytics
  • Managing Data Consistency in Distributed Databases
  • The Impact of Distributed Systems on Digital Transformation Strategies
  • Augmented Reality over Distributed Networks: Performance and Scalability Issues
  • The Application of Distributed Systems in Smart Grid Technology
  • Developing Distributed Applications Using Serverless Architectures
  • The Challenges of Implementing IPv6 in Distributed Networks
  • Distributed Systems for Disaster Recovery: Design and Implementation
  • The Use of Virtual Reality in Distributed Network Environments
  • Security Protocols for Ad Hoc Networks in Emergency Situations
  • The Role of Distributed Networks in Enhancing Mobile Broadband Services
  • Next-Generation Protocols for Enhanced Network Reliability and Performance
  • The Application of Blockchain in Securing Distributed IoT Networks
  • Dynamic Resource Allocation Strategies in Distributed Systems
  • The Integration of Distributed Systems with Existing IT Infrastructure
  • The Future of Autonomous Systems in Distributed Networking
  • The Integration of GIS with Remote Sensing for Environmental Monitoring
  • GIS in Urban Planning: Techniques for Sustainable Development
  • The Role of GIS in Disaster Management and Response Strategies
  • Real-Time GIS Applications in Traffic Management and Route Planning
  • The Use of GIS in Water Resource Management
  • GIS and Public Health: Tracking Epidemics and Healthcare Access
  • Advances in 3D GIS: Technologies and Applications
  • GIS in Agricultural Management: Precision Farming Techniques
  • The Impact of GIS on Biodiversity Conservation Efforts
  • Spatial Data Analysis for Crime Pattern Detection and Prevention
  • GIS in Renewable Energy: Site Selection and Resource Management
  • The Role of GIS in Historical Research and Archaeology
  • GIS and Machine Learning: Integrating Spatial Analysis with Predictive Models
  • Cloud Computing and GIS: Enhancing Accessibility and Data Processing
  • The Application of GIS in Managing Public Transportation Systems
  • GIS in Real Estate: Market Analysis and Property Valuation
  • The Use of GIS for Environmental Impact Assessments
  • Mobile GIS Applications: Development and Usage Trends
  • GIS and Its Role in Smart City Initiatives
  • Privacy Issues in the Use of Geographic Information Systems
  • GIS in Forest Management: Monitoring and Conservation Strategies
  • The Impact of GIS on Tourism: Enhancing Visitor Experiences through Technology
  • GIS in the Insurance Industry: Risk Assessment and Policy Design
  • The Development of Participatory GIS (PGIS) for Community Engagement
  • GIS in Coastal Management: Addressing Erosion and Flood Risks
  • Geospatial Analytics in Retail: Optimizing Location and Consumer Insights
  • GIS for Wildlife Tracking and Habitat Analysis
  • The Use of GIS in Climate Change Studies
  • GIS and Social Media: Analyzing Spatial Trends from User Data
  • The Future of GIS: Augmented Reality and Virtual Reality Applications
  • GIS in Education: Tools for Teaching Geographic Concepts
  • The Role of GIS in Land Use Planning and Zoning
  • GIS for Emergency Medical Services: Optimizing Response Times
  • Open Source GIS Software: Development and Community Contributions
  • GIS and the Internet of Things (IoT): Converging Technologies for Advanced Monitoring
  • GIS for Mineral Exploration: Techniques and Applications
  • The Role of GIS in Municipal Management and Services
  • GIS and Drone Technology: A Synergy for Precision Mapping
  • Spatial Statistics in GIS: Techniques for Advanced Data Analysis
  • Future Trends in GIS: The Integration of AI for Smarter Solutions
  • The Evolution of User Interface (UI) Design: From Desktop to Mobile and Beyond
  • The Role of HCI in Enhancing Accessibility for Disabled Users
  • Virtual Reality (VR) and Augmented Reality (AR) in HCI: New Dimensions of Interaction
  • The Impact of HCI on User Experience (UX) in Software Applications
  • Cognitive Aspects of HCI: Understanding User Perception and Behavior
  • HCI and the Internet of Things (IoT): Designing Interactive Smart Devices
  • The Use of Biometrics in HCI: Security and Usability Concerns
  • HCI in Educational Technologies: Enhancing Learning through Interaction
  • Emotional Recognition and Its Application in HCI
  • The Role of HCI in Wearable Technology: Design and Functionality
  • Advanced Techniques in Voice User Interfaces (VUIs)
  • The Impact of HCI on Social Media Interaction Patterns
  • HCI in Healthcare: Designing User-Friendly Medical Devices and Software
  • HCI and Gaming: Enhancing Player Engagement and Experience
  • The Use of HCI in Robotic Systems: Improving Human-Robot Interaction
  • The Influence of HCI on E-commerce: Optimizing User Journeys and Conversions
  • HCI in Smart Homes: Interaction Design for Automated Environments
  • Multimodal Interaction: Integrating Touch, Voice, and Gesture in HCI
  • HCI and Aging: Designing Technology for Older Adults
  • The Role of HCI in Virtual Teams: Tools and Strategies for Collaboration
  • User-Centered Design: HCI Strategies for Developing User-Focused Software
  • HCI Research Methodologies: Experimental Design and User Studies
  • The Application of HCI Principles in the Design of Public Kiosks
  • The Future of HCI: Integrating Artificial Intelligence for Smarter Interfaces
  • HCI in Transportation: Designing User Interfaces for Autonomous Vehicles
  • Privacy and Ethics in HCI: Addressing User Data Security
  • HCI and Environmental Sustainability: Promoting Eco-Friendly Behaviors
  • Adaptive Interfaces: HCI Design for Personalized User Experiences
  • The Role of HCI in Content Creation: Tools for Artists and Designers
  • HCI for Crisis Management: Designing Systems for Emergency Use
  • The Use of HCI in Sports Technology: Enhancing Training and Performance
  • The Evolution of Haptic Feedback in HCI
  • HCI and Cultural Differences: Designing for Global User Bases
  • The Impact of HCI on Digital Marketing: Creating Engaging User Interactions
  • HCI in Financial Services: Improving User Interfaces for Banking Apps
  • The Role of HCI in Enhancing User Trust in Technology
  • HCI for Public Safety: User Interfaces for Security Systems
  • The Application of HCI in the Film and Television Industry
  • HCI and the Future of Work: Designing Interfaces for Remote Collaboration
  • Innovations in HCI: Exploring New Interaction Technologies and Their Applications
  • Deep Learning Techniques for Advanced Image Segmentation
  • Real-Time Image Processing for Autonomous Driving Systems
  • Image Enhancement Algorithms for Underwater Imaging
  • Super-Resolution Imaging: Techniques and Applications
  • The Role of Image Processing in Remote Sensing and Satellite Imagery Analysis
  • Machine Learning Models for Medical Image Diagnosis
  • The Impact of AI on Photographic Restoration and Enhancement
  • Image Processing in Security Systems: Facial Recognition and Motion Detection
  • Advanced Algorithms for Image Noise Reduction
  • 3D Image Reconstruction Techniques in Tomography
  • Image Processing for Agricultural Monitoring: Crop Disease Detection and Yield Prediction
  • Techniques for Panoramic Image Stitching
  • Video Image Processing: Real-Time Streaming and Data Compression
  • The Application of Image Processing in Printing Technology
  • Color Image Processing: Theory and Practical Applications
  • The Use of Image Processing in Biometrics Identification
  • Computational Photography: Image Processing Techniques in Smartphone Cameras
  • Image Processing for Augmented Reality: Real-time Object Overlay
  • The Development of Image Processing Algorithms for Traffic Control Systems
  • Pattern Recognition and Analysis in Forensic Imaging
  • Adaptive Filtering Techniques in Image Processing
  • Image Processing in Retail: Customer Tracking and Behavior Analysis
  • The Role of Image Processing in Cultural Heritage Preservation
  • Image Segmentation Techniques for Cancer Detection in Medical Imaging
  • High Dynamic Range (HDR) Imaging: Algorithms and Display Techniques
  • Image Classification with Deep Convolutional Neural Networks
  • The Evolution of Edge Detection Algorithms in Image Processing
  • Image Processing for Wildlife Monitoring: Species Recognition and Behavior Analysis
  • Application of Wavelet Transforms in Image Compression
  • Image Processing in Sports: Enhancing Broadcasts and Performance Analysis
  • Optical Character Recognition (OCR) Improvements in Document Scanning
  • Multi-Spectral Imaging for Environmental and Earth Studies
  • Image Processing for Space Exploration: Analysis of Planetary Images
  • Real-Time Image Processing for Event Surveillance
  • The Influence of Quantum Computing on Image Processing Speed and Security
  • Machine Vision in Manufacturing: Defect Detection and Quality Control
  • Image Processing in Neurology: Visualizing Brain Functions
  • Photogrammetry and Image Processing in Geology: 3D Terrain Mapping
  • Advanced Techniques in Image Watermarking for Copyright Protection
  • The Future of Image Processing: Integrating AI for Automated Editing
  • The Evolution of Enterprise Resource Planning (ERP) Systems in the Digital Age
  • Information Systems for Managing Distributed Workforces
  • The Role of Information Systems in Enhancing Supply Chain Management
  • Cybersecurity Measures in Information Systems
  • The Impact of Big Data on Decision Support Systems
  • Blockchain Technology for Information System Security
  • The Development of Sustainable IT Infrastructure in Information Systems
  • The Use of AI in Information Systems for Business Intelligence
  • Information Systems in Healthcare: Improving Patient Care and Data Management
  • The Influence of IoT on Information Systems Architecture
  • Mobile Information Systems: Development and Usability Challenges
  • The Role of Geographic Information Systems (GIS) in Urban Planning
  • Social Media Analytics: Tools and Techniques in Information Systems
  • Information Systems in Education: Enhancing Learning and Administration
  • Cloud Computing Integration into Corporate Information Systems
  • Information Systems Audit: Practices and Challenges
  • User Interface Design and User Experience in Information Systems
  • Privacy and Data Protection in Information Systems
  • The Future of Quantum Computing in Information Systems
  • The Role of Information Systems in Environmental Management
  • Implementing Effective Knowledge Management Systems
  • The Adoption of Virtual Reality in Information Systems
  • The Challenges of Implementing ERP Systems in Multinational Corporations
  • Information Systems for Real-Time Business Analytics
  • The Impact of 5G Technology on Mobile Information Systems
  • Ethical Issues in the Management of Information Systems
  • Information Systems in Retail: Enhancing Customer Experience and Management
  • The Role of Information Systems in Non-Profit Organizations
  • Development of Decision Support Systems for Strategic Planning
  • Information Systems in the Banking Sector: Enhancing Financial Services
  • Risk Management in Information Systems
  • The Integration of Artificial Neural Networks in Information Systems
  • Information Systems and Corporate Governance
  • Information Systems for Disaster Response and Management
  • The Role of Information Systems in Sports Management
  • Information Systems for Public Health Surveillance
  • The Future of Information Systems: Trends and Predictions
  • Information Systems in the Film and Media Industry
  • Business Process Reengineering through Information Systems
  • Implementing Customer Relationship Management (CRM) Systems in E-commerce
  • Emerging Trends in Artificial Intelligence and Machine Learning
  • The Future of Cloud Services and Technology
  • Cybersecurity: Current Threats and Future Defenses
  • The Role of Information Technology in Sustainable Energy Solutions
  • Internet of Things (IoT): From Smart Homes to Smart Cities
  • Blockchain and Its Impact on Information Technology
  • The Use of Big Data Analytics in Predictive Modeling
  • Virtual Reality (VR) and Augmented Reality (AR): The Next Frontier in IT
  • The Challenges of Digital Transformation in Traditional Businesses
  • Wearable Technology: Health Monitoring and Beyond
  • 5G Technology: Implementation and Impacts on IT
  • Biometrics Technology: Uses and Privacy Concerns
  • The Role of IT in Global Health Initiatives
  • Ethical Considerations in the Development of Autonomous Systems
  • Data Privacy in the Age of Information Overload
  • The Evolution of Software Development Methodologies
  • Quantum Computing: The Next Revolution in IT
  • IT Governance: Best Practices and Standards
  • The Integration of AI in Customer Service Technology
  • IT in Manufacturing: Industrial Automation and Robotics
  • The Future of E-commerce: Technology and Trends
  • Mobile Computing: Innovations and Challenges
  • Information Technology in Education: Tools and Trends
  • IT Project Management: Approaches and Tools
  • The Role of IT in Media and Entertainment
  • The Impact of Digital Marketing Technologies on Business Strategies
  • IT in Logistics and Supply Chain Management
  • The Development and Future of Autonomous Vehicles
  • IT in the Insurance Sector: Enhancing Efficiency and Customer Engagement
  • The Role of IT in Environmental Conservation
  • Smart Grid Technology: IT at the Intersection of Energy Management
  • Telemedicine: The Impact of IT on Healthcare Delivery
  • IT in the Agricultural Sector: Innovations and Impact
  • Cyber-Physical Systems: IT in the Integration of Physical and Digital Worlds
  • The Influence of Social Media Platforms on IT Development
  • Data Centers: Evolution, Technologies, and Sustainability
  • IT in Public Administration: Improving Services and Transparency
  • The Role of IT in Sports Analytics
  • Information Technology in Retail: Enhancing the Shopping Experience
  • The Future of IT: Integrating Ethical AI Systems

Internet of Things (IoT) Thesis Topics

  • Enhancing IoT Security: Strategies for Safeguarding Connected Devices
  • IoT in Smart Cities: Infrastructure and Data Management Challenges
  • The Application of IoT in Precision Agriculture: Maximizing Efficiency and Yield
  • IoT and Healthcare: Opportunities for Remote Monitoring and Patient Care
  • Energy Efficiency in IoT: Techniques for Reducing Power Consumption in Devices
  • The Role of IoT in Supply Chain Management and Logistics
  • Real-Time Data Processing Using Edge Computing in IoT Networks
  • Privacy Concerns and Data Protection in IoT Systems
  • The Integration of IoT with Blockchain for Enhanced Security and Transparency
  • IoT in Environmental Monitoring: Systems for Air Quality and Water Safety
  • Predictive Maintenance in Industrial IoT: Strategies and Benefits
  • IoT in Retail: Enhancing Customer Experience through Smart Technology
  • The Development of Standard Protocols for IoT Communication
  • IoT in Smart Homes: Automation and Security Systems
  • The Role of IoT in Disaster Management: Early Warning Systems and Response Coordination
  • Machine Learning Techniques for IoT Data Analytics
  • IoT in Automotive: The Future of Connected and Autonomous Vehicles
  • The Impact of 5G on IoT: Enhancements in Speed and Connectivity
  • IoT Device Lifecycle Management: From Creation to Decommissioning
  • IoT in Public Safety: Applications for Emergency Response and Crime Prevention
  • The Ethics of IoT: Balancing Innovation with Consumer Rights
  • IoT and the Future of Work: Automation and Labor Market Shifts
  • Designing User-Friendly Interfaces for IoT Applications
  • IoT in the Energy Sector: Smart Grids and Renewable Energy Integration
  • Quantum Computing and IoT: Potential Impacts and Applications
  • The Role of AI in Enhancing IoT Solutions
  • IoT for Elderly Care: Technologies for Health and Mobility Assistance
  • IoT in Education: Enhancing Classroom Experiences and Learning Outcomes
  • Challenges in Scaling IoT Infrastructure for Global Coverage
  • The Economic Impact of IoT: Industry Transformations and New Business Models
  • IoT and Tourism: Enhancing Visitor Experiences through Connected Technologies
  • Data Fusion Techniques in IoT: Integrating Diverse Data Sources
  • IoT in Aquaculture: Monitoring and Managing Aquatic Environments
  • Wireless Technologies for IoT: Comparing LoRa, Zigbee, and NB-IoT
  • IoT and Intellectual Property: Navigating the Legal Landscape
  • IoT in Sports: Enhancing Training and Audience Engagement
  • Building Resilient IoT Systems against Cyber Attacks
  • IoT for Waste Management: Innovations and System Implementations
  • IoT in Agriculture: Drones and Sensors for Crop Monitoring
  • The Role of IoT in Cultural Heritage Preservation: Monitoring and Maintenance
  • Advanced Algorithms for Supervised and Unsupervised Learning
  • Machine Learning in Genomics: Predicting Disease Propensity and Treatment Outcomes
  • The Use of Neural Networks in Image Recognition and Analysis
  • Reinforcement Learning: Applications in Robotics and Autonomous Systems
  • The Role of Machine Learning in Natural Language Processing and Linguistic Analysis
  • Deep Learning for Predictive Analytics in Business and Finance
  • Machine Learning for Cybersecurity: Detection of Anomalies and Malware
  • Ethical Considerations in Machine Learning: Bias and Fairness
  • The Integration of Machine Learning with IoT for Smart Device Management
  • Transfer Learning: Techniques and Applications in New Domains
  • The Application of Machine Learning in Environmental Science
  • Machine Learning in Healthcare: Diagnosing Conditions from Medical Images
  • The Use of Machine Learning in Algorithmic Trading and Stock Market Analysis
  • Machine Learning in Social Media: Sentiment Analysis and Trend Prediction
  • Quantum Machine Learning: Merging Quantum Computing with AI
  • Feature Engineering and Selection in Machine Learning
  • Machine Learning for Enhancing User Experience in Mobile Applications
  • The Impact of Machine Learning on Digital Marketing Strategies
  • Machine Learning for Energy Consumption Forecasting and Optimization
  • The Role of Machine Learning in Enhancing Network Security Protocols
  • Scalability and Efficiency of Machine Learning Algorithms
  • Machine Learning in Drug Discovery and Pharmaceutical Research
  • The Application of Machine Learning in Sports Analytics
  • Machine Learning for Real-Time Decision-Making in Autonomous Vehicles
  • The Use of Machine Learning in Predicting Geographical and Meteorological Events
  • Machine Learning for Educational Data Mining and Learning Analytics
  • The Role of Machine Learning in Audio Signal Processing
  • Predictive Maintenance in Manufacturing Through Machine Learning
  • Machine Learning and Its Implications for Privacy and Surveillance
  • The Application of Machine Learning in Augmented Reality Systems
  • Deep Learning Techniques in Medical Diagnosis: Challenges and Opportunities
  • The Use of Machine Learning in Video Game Development
  • Machine Learning for Fraud Detection in Financial Services
  • The Role of Machine Learning in Agricultural Optimization and Management
  • The Impact of Machine Learning on Content Personalization and Recommendation Systems
  • Machine Learning in Legal Tech: Document Analysis and Case Prediction
  • Adaptive Learning Systems: Tailoring Education Through Machine Learning
  • Machine Learning in Space Exploration: Analyzing Data from Space Missions
  • Machine Learning for Public Sector Applications: Improving Services and Efficiency
  • The Future of Machine Learning: Integrating Explainable AI
  • Innovations in Convolutional Neural Networks for Image and Video Analysis
  • Recurrent Neural Networks: Applications in Sequence Prediction and Analysis
  • The Role of Neural Networks in Predicting Financial Market Trends
  • Deep Neural Networks for Enhanced Speech Recognition Systems
  • Neural Networks in Medical Imaging: From Detection to Diagnosis
  • Generative Adversarial Networks (GANs): Applications in Art and Media
  • The Use of Neural Networks in Autonomous Driving Technologies
  • Neural Networks for Real-Time Language Translation
  • The Application of Neural Networks in Robotics: Sensory Data and Movement Control
  • Neural Network Optimization Techniques: Overcoming Overfitting and Underfitting
  • The Integration of Neural Networks with Blockchain for Data Security
  • Neural Networks in Climate Modeling and Weather Forecasting
  • The Use of Neural Networks in Enhancing Internet of Things (IoT) Devices
  • Graph Neural Networks: Applications in Social Network Analysis and Beyond
  • The Impact of Neural Networks on Augmented Reality Experiences
  • Neural Networks for Anomaly Detection in Network Security
  • The Application of Neural Networks in Bioinformatics and Genomic Data Analysis
  • Capsule Neural Networks: Improving the Robustness and Interpretability of Deep Learning
  • The Role of Neural Networks in Consumer Behavior Analysis
  • Neural Networks in Energy Sector: Forecasting and Optimization
  • The Evolution of Neural Network Architectures for Efficient Learning
  • The Use of Neural Networks in Sentiment Analysis: Techniques and Challenges
  • Deep Reinforcement Learning: Strategies for Advanced Decision-Making Systems
  • Neural Networks for Precision Medicine: Tailoring Treatments to Individual Genetic Profiles
  • The Use of Neural Networks in Virtual Assistants: Enhancing Natural Language Understanding
  • The Impact of Neural Networks on Pharmaceutical Research
  • Neural Networks for Supply Chain Management: Prediction and Automation
  • The Application of Neural Networks in E-commerce: Personalization and Recommendation Systems
  • Neural Networks for Facial Recognition: Advances and Ethical Considerations
  • The Role of Neural Networks in Educational Technologies
  • The Use of Neural Networks in Predicting Economic Trends
  • Neural Networks in Sports: Analyzing Performance and Strategy
  • The Impact of Neural Networks on Digital Security Systems
  • Neural Networks for Real-Time Video Surveillance Analysis
  • The Integration of Neural Networks in Edge Computing Devices
  • Neural Networks for Industrial Automation: Improving Efficiency and Accuracy
  • The Future of Neural Networks: Towards More General AI Applications
  • Neural Networks in Art and Design: Creating New Forms of Expression
  • The Role of Neural Networks in Enhancing Public Health Initiatives
  • The Future of Neural Networks: Challenges in Scalability and Generalization
  • The Evolution of Programming Paradigms: Functional vs. Object-Oriented Programming
  • Advances in Compiler Design and Optimization Techniques
  • The Impact of Programming Languages on Software Security
  • Developing Programming Languages for Quantum Computing
  • Machine Learning in Automated Code Generation and Optimization
  • The Role of Programming in Developing Scalable Cloud Applications
  • The Future of Web Development: New Frameworks and Technologies
  • Cross-Platform Development: Best Practices in Mobile App Programming
  • The Influence of Programming Techniques on Big Data Analytics
  • Real-Time Systems Programming: Challenges and Solutions
  • The Integration of Programming with Blockchain Technology
  • Programming for IoT: Languages and Tools for Device Communication
  • Secure Coding Practices: Preventing Cyber Attacks through Software Design
  • The Role of Programming in Data Visualization and User Interface Design
  • Advances in Game Programming: Graphics, AI, and Network Play
  • The Impact of Programming on Digital Media and Content Creation
  • Programming Languages for Robotics: Trends and Future Directions
  • The Use of Artificial Intelligence in Enhancing Programming Productivity
  • Programming for Augmented and Virtual Reality: New Challenges and Techniques
  • Ethical Considerations in Programming: Bias, Fairness, and Transparency
  • The Future of Programming Education: Interactive and Adaptive Learning Models
  • Programming for Wearable Technology: Special Considerations and Challenges
  • The Evolution of Programming in Financial Technology
  • Functional Programming in Enterprise Applications
  • Memory Management Techniques in Programming: From Garbage Collection to Manual Control
  • The Role of Open Source Programming in Accelerating Innovation
  • The Impact of Programming on Network Security and Cryptography
  • Developing Accessible Software: Programming for Users with Disabilities
  • Programming Language Theories: New Models and Approaches
  • The Challenges of Legacy Code: Strategies for Modernization and Integration
  • Energy-Efficient Programming: Optimizing Code for Green Computing
  • Multithreading and Concurrency: Advanced Programming Techniques
  • The Impact of Programming on Computational Biology and Bioinformatics
  • The Role of Scripting Languages in Automating System Administration
  • Programming and the Future of Quantum Resistant Cryptography
  • Code Review and Quality Assurance: Techniques and Tools
  • Adaptive and Predictive Programming for Dynamic Environments
  • The Role of Programming in Enhancing E-commerce Technology
  • Programming for Cyber-Physical Systems: Bridging the Gap Between Digital and Physical
  • The Influence of Programming Languages on Computational Efficiency and Performance
  • Quantum Algorithms: Development and Applications Beyond Shor’s and Grover’s Algorithms
  • The Role of Quantum Computing in Solving Complex Biological Problems
  • Quantum Cryptography: New Paradigms for Secure Communication
  • Error Correction Techniques in Quantum Computing
  • Quantum Computing and Its Impact on Artificial Intelligence
  • The Integration of Classical and Quantum Computing: Hybrid Models
  • Quantum Machine Learning: Theoretical Foundations and Practical Applications
  • Quantum Computing Hardware: Advances in Qubit Technology
  • The Application of Quantum Computing in Financial Modeling and Risk Assessment
  • Quantum Networking: Establishing Secure Quantum Communication Channels
  • The Future of Drug Discovery: Applications of Quantum Computing
  • Quantum Computing in Cryptanalysis: Threats to Current Cryptography Standards
  • Simulation of Quantum Systems for Material Science
  • Quantum Computing for Optimization Problems in Logistics and Manufacturing
  • Theoretical Limits of Quantum Computing: Understanding Quantum Complexity
  • Quantum Computing and the Future of Search Algorithms
  • The Role of Quantum Computing in Climate Science and Environmental Modeling
  • Quantum Annealing vs. Universal Quantum Computing: Comparative Studies
  • Implementing Quantum Algorithms in Quantum Programming Languages
  • The Impact of Quantum Computing on Public Key Cryptography
  • Quantum Entanglement: Experiments and Applications in Quantum Networks
  • Scalability Challenges in Quantum Processors
  • The Ethics and Policy Implications of Quantum Computing
  • Quantum Computing in Space Exploration and Astrophysics
  • The Role of Quantum Computing in Developing Next-Generation AI Systems
  • Quantum Computing in the Energy Sector: Applications in Smart Grids and Nuclear Fusion
  • Noise and Decoherence in Quantum Computers: Overcoming Practical Challenges
  • Quantum Computing for Predicting Economic Market Trends
  • Quantum Sensors: Enhancing Precision in Measurement and Imaging
  • The Future of Quantum Computing Education and Workforce Development
  • Quantum Computing in Cybersecurity: Preparing for a Post-Quantum World
  • Quantum Computing and the Internet of Things: Potential Intersections
  • Practical Quantum Computing: From Theory to Real-World Applications
  • Quantum Supremacy: Milestones and Future Goals
  • The Role of Quantum Computing in Genetics and Genomics
  • Quantum Computing for Material Discovery and Design
  • The Challenges of Quantum Programming Languages and Environments
  • Quantum Computing in Art and Creative Industries
  • The Global Race for Quantum Computing Supremacy: Technological and Political Aspects
  • Quantum Computing and Its Implications for Software Engineering
  • Advances in Humanoid Robotics: New Developments and Challenges
  • Robotics in Healthcare: From Surgery to Rehabilitation
  • The Integration of AI in Robotics: Enhanced Autonomy and Learning Capabilities
  • Swarm Robotics: Coordination Strategies and Applications
  • The Use of Robotics in Hazardous Environments: Deep Sea and Space Exploration
  • Soft Robotics: Materials, Design, and Applications
  • Robotics in Agriculture: Automation of Farming and Harvesting Processes
  • The Role of Robotics in Manufacturing: Increased Efficiency and Flexibility
  • Ethical Considerations in the Deployment of Robots in Human Environments
  • Autonomous Vehicles: Technological Advances and Regulatory Challenges
  • Robotic Assistants for the Elderly and Disabled: Improving Quality of Life
  • The Use of Robotics in Education: Teaching Science, Technology, Engineering, and Math (STEM)
  • Robotics and Computer Vision: Enhancing Perception and Decision Making
  • The Impact of Robotics on Employment and the Workforce
  • The Development of Robotic Systems for Environmental Monitoring and Conservation
  • Machine Learning Techniques for Robotic Perception and Navigation
  • Advances in Robotic Surgery: Precision and Outcomes
  • Human-Robot Interaction: Building Trust and Cooperation
  • Robotics in Retail: Automated Warehousing and Customer Service
  • Energy-Efficient Robots: Design and Utilization
  • Robotics in Construction: Automation and Safety Improvements
  • The Role of Robotics in Disaster Response and Recovery Operations
  • The Application of Robotics in Art and Creative Industries
  • Robotics and the Future of Personal Transportation
  • Ethical AI in Robotics: Ensuring Safe and Fair Decision-Making
  • The Use of Robotics in Logistics: Drones and Autonomous Delivery Vehicles
  • Robotics in the Food Industry: From Production to Service
  • The Integration of IoT with Robotics for Enhanced Connectivity
  • Wearable Robotics: Exoskeletons for Rehabilitation and Enhanced Mobility
  • The Impact of Robotics on Privacy and Security
  • Robotic Pet Companions: Social Robots and Their Psychological Effects
  • Robotics for Planetary Exploration and Colonization
  • Underwater Robotics: Innovations in Oceanography and Marine Biology
  • Advances in Robotics Programming Languages and Tools
  • The Role of Robotics in Minimizing Human Exposure to Contaminants and Pathogens
  • Collaborative Robots (Cobots): Working Alongside Humans in Shared Spaces
  • The Use of Robotics in Entertainment and Sports
  • Robotics and Machine Ethics: Programming Moral Decision-Making
  • The Future of Military Robotics: Opportunities and Challenges
  • Sustainable Robotics: Reducing the Environmental Impact of Robotic Systems
  • Agile Methodologies: Evolution and Future Trends
  • DevOps Practices: Improving Software Delivery and Lifecycle Management
  • The Impact of Microservices Architecture on Software Development
  • Containerization Technologies: Docker, Kubernetes, and Beyond
  • Software Quality Assurance: Modern Techniques and Tools
  • The Role of Artificial Intelligence in Automated Software Testing
  • Blockchain Applications in Software Development and Security
  • The Integration of Continuous Integration and Continuous Deployment (CI/CD) in Software Projects
  • Cybersecurity in Software Engineering: Best Practices for Secure Coding
  • Low-Code and No-Code Development: Implications for Professional Software Development
  • The Future of Software Engineering Education
  • Software Sustainability: Developing Green Software and Reducing Carbon Footprints
  • The Role of Software Engineering in Healthcare: Telemedicine and Patient Data Management
  • Privacy by Design: Incorporating Privacy Features at the Development Stage
  • The Impact of Quantum Computing on Software Engineering
  • Software Engineering for Augmented and Virtual Reality: Challenges and Innovations
  • Cloud-Native Applications: Design, Development, and Deployment
  • Software Project Management: Agile vs. Traditional Approaches
  • Open Source Software: Community Engagement and Project Sustainability
  • The Evolution of Graphical User Interfaces in Application Development
  • The Challenges of Integrating IoT Devices into Software Systems
  • Ethical Issues in Software Engineering: Bias, Accountability, and Regulation
  • Software Engineering for Autonomous Vehicles: Safety and Regulatory Considerations
  • Big Data Analytics in Software Development: Enhancing Decision-Making Processes
  • The Future of Mobile App Development: Trends and Technologies
  • The Role of Software Engineering in Artificial Intelligence: Frameworks and Algorithms
  • Performance Optimization in Software Applications
  • Adaptive Software Development: Responding to Changing User Needs
  • Software Engineering in Financial Services: Compliance and Security Challenges
  • User Experience (UX) Design in Software Engineering
  • The Role of Software Engineering in Smart Cities: Infrastructure and Services
  • The Impact of 5G on Software Development and Deployment
  • Real-Time Systems in Software Engineering: Design and Implementation Challenges
  • Cross-Platform Development Challenges: Ensuring Consistency and Performance
  • Software Testing Automation: Tools and Trends
  • The Integration of Cyber-Physical Systems in Software Engineering
  • Software Engineering in the Entertainment Industry: Game Development and Beyond
  • The Application of Machine Learning in Predicting Software Bugs
  • The Role of Software Engineering in Cybersecurity Defense Strategies
  • Accessibility in Software Engineering: Creating Inclusive and Usable Software
  • Progressive Web Apps (PWAs): Advantages and Implementation Challenges
  • The Future of Web Accessibility: Standards and Practices
  • Single-Page Applications (SPAs) vs. Multi-Page Applications (MPAs): Performance and Usability
  • The Impact of Serverless Computing on Web Development
  • The Evolution of CSS for Modern Web Design
  • Security Best Practices in Web Development: Defending Against XSS and CSRF Attacks
  • The Role of Web Development in Enhancing E-commerce User Experience
  • The Use of Artificial Intelligence in Web Personalization and User Engagement
  • The Future of Web APIs: Standards, Security, and Scalability
  • Responsive Web Design: Techniques and Trends
  • JavaScript Frameworks: Vue.js, React.js, and Angular – A Comparative Analysis
  • Web Development for IoT: Interfaces and Connectivity Solutions
  • The Impact of 5G on Web Development and User Experiences
  • The Use of Blockchain Technology in Web Development for Enhanced Security
  • Web Development in the Cloud: Using AWS, Azure, and Google Cloud
  • Content Management Systems (CMS): Trends and Future Developments
  • The Application of Web Development in Virtual and Augmented Reality
  • The Importance of Web Performance Optimization: Tools and Techniques
  • Sustainable Web Design: Practices for Reducing Energy Consumption
  • The Role of Web Development in Digital Marketing: SEO and Social Media Integration
  • Headless CMS: Benefits and Challenges for Developers and Content Creators
  • The Future of Web Typography: Design, Accessibility, and Performance
  • Web Development and Data Protection: Complying with GDPR and Other Regulations
  • Real-Time Web Communication: Technologies like WebSockets and WebRTC
  • Front-End Development Tools: Efficiency and Innovation in Workflow
  • The Challenges of Migrating Legacy Systems to Modern Web Architectures
  • Microfrontends Architecture: Designing Scalable and Decoupled Web Applications
  • The Impact of Cryptocurrencies on Web Payment Systems
  • User-Centered Design in Web Development: Methods for Engaging Users
  • The Role of Web Development in Business Intelligence: Dashboards and Reporting Tools
  • Web Development for Mobile Platforms: Optimization and Best Practices
  • The Evolution of E-commerce Platforms: From Web to Mobile Commerce
  • Web Security in E-commerce: Protecting Transactions and User Data
  • Dynamic Web Content: Server-Side vs. Client-Side Rendering
  • The Future of Full Stack Development: Trends and Skills
  • Web Design Psychology: How Design Influences User Behavior
  • The Role of Web Development in the Non-Profit Sector: Fundraising and Community Engagement
  • The Integration of AI Chatbots in Web Development
  • The Use of Motion UI in Web Design: Enhancing Aesthetics and User Interaction
  • The Future of Web Development: Predictions and Emerging Technologies

We trust that this comprehensive list of computer science thesis topics will serve as a valuable starting point for your research endeavors. With 1000 unique and carefully selected topics distributed across 25 key areas of computer science, students are equipped to tackle complex questions and contribute meaningful advancements to the field. As you proceed to select your thesis topic, consider not only your personal interests and career goals but also the potential impact of your research. We encourage you to explore these topics thoroughly and choose one that will not only challenge you but also push the boundaries of technology and innovation.

The Range of Computer Science Thesis Topics

Computer science stands as a dynamic and ever-evolving field that continuously reshapes how we interact with the world. At its core, the discipline encompasses not just the study of algorithms and computation, but a broad spectrum of practical and theoretical knowledge areas that drive innovation in various sectors. This article aims to explore the rich landscape of computer science thesis topics, offering students and researchers a glimpse into the potential areas of study that not only challenge the intellect but also contribute significantly to technological progress. As we delve into the current issues, recent trends, and future directions of computer science, it becomes evident that the possibilities for research are both vast and diverse. Whether you are intrigued by the complexities of artificial intelligence, the robust architecture of networks and systems, or the innovative approaches in cybersecurity, computer science offers a fertile ground for developing thesis topics that are as impactful as they are intellectually stimulating.

Current Issues in Computer Science

One of the prominent current issues in computer science revolves around data security and privacy. As digital transformation accelerates across industries, the massive influx of data generated poses significant challenges in terms of its protection and ethical use. Cybersecurity threats have become more sophisticated, with data breaches and cyber-attacks causing major concerns for organizations worldwide. This ongoing battle demands continuous improvements in security protocols and the development of robust cybersecurity measures. Computer science thesis topics in this area can explore new cryptographic methods, intrusion detection systems, and secure communication protocols to fortify digital defenses. Research could also delve into the ethical implications of data collection and use, proposing frameworks that ensure privacy while still leveraging data for innovation.

Another critical issue facing the field of computer science is the ethical development and deployment of artificial intelligence (AI) systems. As AI technologies become more integrated into daily life and critical infrastructure, concerns about bias, fairness, and accountability in AI systems have intensified. Thesis topics could focus on developing algorithms that address these ethical concerns, including techniques for reducing bias in machine learning models and methods for increasing transparency and explainability in AI decisions. This research is crucial for ensuring that AI technologies promote fairness and do not perpetuate or exacerbate existing societal inequalities.

Furthermore, the rapid pace of technological change presents a challenge in terms of sustainability and environmental impact. The energy consumption of large data centers, the carbon footprint of producing and disposing of electronic waste, and the broader effects of high-tech innovations on the environment are significant concerns within computer science. Thesis research in this domain could focus on creating more energy-efficient computing methods, developing algorithms that reduce power consumption, or innovating recycling technologies that address the issue of e-waste. This research not only contributes to the field of computer science but also plays a crucial role in ensuring that technological advancement does not come at an unsustainable cost to the environment.

These current issues highlight the dynamic nature of computer science and its direct impact on society. Addressing these challenges through focused research and innovative thesis topics not only advances the field but also contributes to resolving some of the most pressing problems facing our global community today.

Recent Trends in Computer Science

In recent years, computer science has witnessed significant advancements in the integration of artificial intelligence (AI) and machine learning (ML) across various sectors, marking one of the most exciting trends in the field. These technologies are not just reshaping traditional industries but are also at the forefront of driving innovations in areas like healthcare, finance, and autonomous systems. Thesis topics within this trend could explore the development of advanced ML algorithms that enhance predictive analytics, improve automated decision-making, or refine natural language processing capabilities. Additionally, AI’s role in ethical decision-making and its societal impacts offers a rich vein of inquiry for research, focusing on mitigating biases and ensuring that AI systems operate transparently and justly.

Another prominent trend in computer science is the rapid growth of blockchain technology beyond its initial application in cryptocurrencies. Blockchain is proving its potential in creating more secure, decentralized, and transparent networks for a variety of applications, from enhancing supply chain logistics to revolutionizing digital identity verification processes. Computer science thesis topics could investigate novel uses of blockchain for ensuring data integrity in digital transactions, enhancing cybersecurity measures, or even developing new frameworks for blockchain integration into existing technological infrastructures. The exploration of blockchain’s scalability, speed, and energy consumption also presents critical research opportunities that are timely and relevant.

Furthermore, the expansion of the Internet of Things (IoT) continues to be a significant trend, with more devices becoming connected every day, leading to increasingly smart environments. This proliferation poses unique challenges and opportunities for computer science research, particularly in terms of scalability, security, and new data management strategies. Thesis topics might focus on optimizing network protocols to handle the massive influx of data from IoT devices, developing solutions to safeguard against IoT-specific security vulnerabilities, or innovative applications of IoT in urban planning, smart homes, or healthcare. Research in this area is crucial for advancing the efficiency and functionality of IoT systems and for ensuring they can be safely and effectively integrated into modern life.

These recent trends underscore the vibrant and ever-evolving nature of computer science, reflecting its capacity to influence and transform an array of sectors through technological innovation. The continual emergence of new research topics within these trends not only enriches the academic discipline but also provides substantial benefits to society by addressing practical challenges and enhancing the capabilities of technology in everyday life.

Future Directions in Computer Science

As we look toward the future, one of the most anticipated areas in computer science is the advancement of quantum computing. This emerging technology promises to revolutionize problem-solving in fields that require immense computational power, such as cryptography, drug discovery, and complex system modeling. Quantum computing has the potential to process tasks at speeds unachievable by classical computers, offering breakthroughs in materials science and encryption methods. Computer science thesis topics might explore the theoretical underpinnings of quantum algorithms, the development of quantum-resistant cryptographic systems, or practical applications of quantum computing in industry-specific scenarios. Research in this area not only contributes to the foundational knowledge of quantum mechanics but also paves the way for its integration into mainstream computing, marking a significant leap forward in computational capabilities.

Another promising direction in computer science is the advancement of autonomous systems, particularly in robotics and vehicle automation. The future of autonomous technologies hinges on improving their safety, reliability, and decision-making processes under uncertain conditions. Thesis topics could focus on the enhancement of machine perception through computer vision and sensor fusion, the development of more sophisticated AI-driven decision frameworks, or ethical considerations in the deployment of autonomous systems. As these technologies become increasingly prevalent, research will play a crucial role in addressing the societal and technical challenges they present, ensuring their beneficial integration into daily life and industry operations.

Additionally, the ongoing expansion of artificial intelligence applications poses significant future directions for research, especially in the realm of AI ethics and policy. As AI systems become more capable and widespread, their impact on privacy, employment, and societal norms continues to grow. Future thesis topics might delve into the development of guidelines and frameworks for responsible AI, studies on the impact of AI on workforce dynamics, or innovations in transparent and fair AI systems. This research is vital for guiding the ethical evolution of AI technologies, ensuring they enhance societal well-being without diminishing human dignity or autonomy.

These future directions in computer science not only highlight the field’s potential for substantial technological advancements but also underscore the importance of thoughtful consideration of their broader implications. By exploring these areas in depth, computer science research can lead the way in not just technological innovation, but also in shaping a future where technology and ethics coexist harmoniously for the betterment of society.

In conclusion, the field of computer science is not only foundational to the technological advancements that characterize the modern age but also crucial in solving some of the most pressing challenges of our time. The potential thesis topics discussed in this article reflect a mere fraction of the opportunities that lie in the realms of theory, application, and innovation within this expansive field. As emerging technologies such as quantum computing, artificial intelligence, and blockchain continue to evolve, they open new avenues for research that could potentially redefine existing paradigms. For students embarking on their thesis journey, it is essential to choose a topic that not only aligns with their academic passions but also contributes to the ongoing expansion of computer science knowledge. By pushing the boundaries of what is known and exploring uncharted territories, students can leave a lasting impact on the field and pave the way for future technological breakthroughs. As we look forward, it’s clear that computer science will continue to be a key driver of change, making it an exciting and rewarding area for academic and professional growth.

Thesis Writing Services by iResearchNet

At iResearchNet, we specialize in providing exceptional thesis writing services tailored to meet the diverse needs of students, particularly those pursuing advanced topics in computer science. Understanding the pivotal role a thesis plays in a student’s academic career, we offer a suite of services designed to assist students in crafting papers that are not only well-researched and insightful but also perfectly aligned with their academic objectives. Here are the key features of our thesis writing services:

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bachelor thesis topics software engineering

Princeton University

  • Advisers & Contacts
  • Bachelor of Arts & Bachelor of Science in Engineering
  • Prerequisites
  • Declaring Computer Science for AB Students
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  • Class of '25, '26 & '27 - Departmental Requirements
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Suggested Undergraduate Research Topics

bachelor thesis topics software engineering

How to Contact Faculty for IW/Thesis Advising

Send the professor an e-mail. When you write a professor, be clear that you want a meeting regarding a senior thesis or one-on-one IW project, and briefly describe the topic or idea that you want to work on. Check the faculty listing for email addresses.

Parastoo Abtahi, Room 419

Available for single-semester IW and senior thesis advising, 2024-2025

  • Research Areas: Human-Computer Interaction (HCI), Augmented Reality (AR), and Spatial Computing
  • Input techniques for on-the-go interaction (e.g., eye-gaze, microgestures, voice) with a focus on uncertainty, disambiguation, and privacy.
  • Minimal and timely multisensory output (e.g., spatial audio, haptics) that enables users to attend to their physical environment and the people around them, instead of a 2D screen.
  • Interaction with intelligent systems (e.g., IoT, robots) situated in physical spaces with a focus on updating users’ mental model despite the complexity and dynamicity of these systems.

Ryan Adams, Room 411

Research areas:

  • Machine learning driven design
  • Generative models for structured discrete objects
  • Approximate inference in probabilistic models
  • Accelerating solutions to partial differential equations
  • Innovative uses of automatic differentiation
  • Modeling and optimizing 3d printing and CNC machining

Andrew Appel, Room 209

Available for Fall 2024 IW advising, only

  • Research Areas: Formal methods, programming languages, compilers, computer security.
  • Software verification (for which taking COS 326 / COS 510 is helpful preparation)
  • Game theory of poker or other games (for which COS 217 / 226 are helpful)
  • Computer game-playing programs (for which COS 217 / 226)
  •  Risk-limiting audits of elections (for which ORF 245 or other knowledge of probability is useful)

Sanjeev Arora, Room 407

  • Theoretical machine learning, deep learning and its analysis, natural language processing. My advisees would typically have taken a course in algorithms (COS423 or COS 521 or equivalent) and a course in machine learning.
  • Show that finding approximate solutions to NP-complete problems is also NP-complete (i.e., come up with NP-completeness reductions a la COS 487). 
  • Experimental Algorithms: Implementing and Evaluating Algorithms using existing software packages. 
  • Studying/designing provable algorithms for machine learning and implementions using packages like scipy and MATLAB, including applications in Natural language processing and deep learning.
  • Any topic in theoretical computer science.

David August, Room 221

Not available for IW or thesis advising, 2024-2025

  • Research Areas: Computer Architecture, Compilers, Parallelism
  • Containment-based approaches to security:  We have designed and tested a simple hardware+software containment mechanism that stops incorrect communication resulting from faults, bugs, or exploits from leaving the system.   Let's explore ways to use containment to solve real problems.  Expect to work with corporate security and technology decision-makers.
  • Parallelism: Studies show much more parallelism than is currently realized in compilers and architectures.  Let's find ways to realize this parallelism.
  • Any other interesting topic in computer architecture or compilers. 

Mark Braverman, 194 Nassau St., Room 231

  • Research Areas: computational complexity, algorithms, applied probability, computability over the real numbers, game theory and mechanism design, information theory.
  • Topics in computational and communication complexity.
  • Applications of information theory in complexity theory.
  • Algorithms for problems under real-life assumptions.
  • Game theory, network effects
  • Mechanism design (could be on a problem proposed by the student)

Sebastian Caldas, 221 Nassau Street, Room 105

  • Research Areas: collaborative learning, machine learning for healthcare. Typically, I will work with students that have taken COS324.
  • Methods for collaborative and continual learning.
  • Machine learning for healthcare applications.

Bernard Chazelle, 194 Nassau St., Room 301

  • Research Areas: Natural Algorithms, Computational Geometry, Sublinear Algorithms. 
  • Natural algorithms (flocking, swarming, social networks, etc).
  • Sublinear algorithms
  • Self-improving algorithms
  • Markov data structures

Danqi Chen, Room 412

  • My advisees would be expected to have taken a course in machine learning and ideally have taken COS484 or an NLP graduate seminar.
  • Representation learning for text and knowledge bases
  • Pre-training and transfer learning
  • Question answering and reading comprehension
  • Information extraction
  • Text summarization
  • Any other interesting topics related to natural language understanding/generation

Marcel Dall'Agnol, Corwin 034

  • Research Areas: Theoretical computer science. (Specifically, quantum computation, sublinear algorithms, complexity theory, interactive proofs and cryptography)
  • Research Areas: Machine learning

Jia Deng, Room 423

  •  Research Areas: Computer Vision, Machine Learning.
  • Object recognition and action recognition
  • Deep Learning, autoML, meta-learning
  • Geometric reasoning, logical reasoning

Adji Bousso Dieng, Room 406

  • Research areas: Vertaix is a research lab at Princeton University led by Professor Adji Bousso Dieng. We work at the intersection of artificial intelligence (AI) and the natural sciences. The models and algorithms we develop are motivated by problems in those domains and contribute to advancing methodological research in AI. We leverage tools in statistical machine learning and deep learning in developing methods for learning with the data, of various modalities, arising from the natural sciences.

Robert Dondero, Corwin Hall, Room 038

  • Research Areas:  Software engineering; software engineering education.
  • Develop or evaluate tools to facilitate student learning in undergraduate computer science courses at Princeton, and beyond.
  • In particular, can code critiquing tools help students learn about software quality?

Zeev Dvir, 194 Nassau St., Room 250

  • Research Areas: computational complexity, pseudo-randomness, coding theory and discrete mathematics.
  • Independent Research: I have various research problems related to Pseudorandomness, Coding theory, Complexity and Discrete mathematics - all of which require strong mathematical background. A project could also be based on writing a survey paper describing results from a few theory papers revolving around some particular subject.

Benjamin Eysenbach, Room 416

  • Research areas: reinforcement learning, machine learning. My advisees would typically have taken COS324.
  • Using RL algorithms to applications in science and engineering.
  • Emergent behavior of RL algorithms on high-fidelity robotic simulators.
  • Studying how architectures and representations can facilitate generalization.

Christiane Fellbaum, 1-S-14 Green

  • Research Areas: theoretical and computational linguistics, word sense disambiguation, lexical resource construction, English and multilingual WordNet(s), ontology
  • Anything having to do with natural language--come and see me with/for ideas suitable to your background and interests. Some topics students have worked on in the past:
  • Developing parsers, part-of-speech taggers, morphological analyzers for underrepresented languages (you don't have to know the language to develop such tools!)
  • Quantitative approaches to theoretical linguistics questions
  • Extensions and interfaces for WordNet (English and WN in other languages),
  • Applications of WordNet(s), including:
  • Foreign language tutoring systems,
  • Spelling correction software,
  • Word-finding/suggestion software for ordinary users and people with memory problems,
  • Machine Translation 
  • Sentiment and Opinion detection
  • Automatic reasoning and inferencing
  • Collaboration with professors in the social sciences and humanities ("Digital Humanities")

Adam Finkelstein, Room 424 

  • Research Areas: computer graphics, audio.

Robert S. Fish, Corwin Hall, Room 037

  • Networking and telecommunications
  • Learning, perception, and intelligence, artificial and otherwise;
  • Human-computer interaction and computer-supported cooperative work
  • Online education, especially in Computer Science Education
  • Topics in research and development innovation methodologies including standards, open-source, and entrepreneurship
  • Distributed autonomous organizations and related blockchain technologies

Michael Freedman, Room 308 

  • Research Areas: Distributed systems, security, networking
  • Projects related to streaming data analysis, datacenter systems and networks, untrusted cloud storage and applications. Please see my group website at for current research projects.

Ruth Fong, Room 032

  • Research Areas: computer vision, machine learning, deep learning, interpretability, explainable AI, fairness and bias in AI
  • Develop a technique for understanding AI models
  • Design a AI model that is interpretable by design
  • Build a paradigm for detecting and/or correcting failure points in an AI model
  • Analyze an existing AI model and/or dataset to better understand its failure points
  • Build a computer vision system for another domain (e.g., medical imaging, satellite data, etc.)
  • Develop a software package for explainable AI
  • Adapt explainable AI research to a consumer-facing problem

Note: I am happy to advise any project if there's a sufficient overlap in interest and/or expertise; please reach out via email to chat about project ideas.

Tom Griffiths, Room 405

Available for Fall 2024 single-semester IW advising, only

Research areas: computational cognitive science, computational social science, machine learning and artificial intelligence

Note: I am open to projects that apply ideas from computer science to understanding aspects of human cognition in a wide range of areas, from decision-making to cultural evolution and everything in between. For example, we have current projects analyzing chess game data and magic tricks, both of which give us clues about how human minds work. Students who have expertise or access to data related to games, magic, strategic sports like fencing, or other quantifiable domains of human behavior feel free to get in touch.

Aarti Gupta, Room 220

  • Research Areas: Formal methods, program analysis, logic decision procedures
  • Finding bugs in open source software using automatic verification tools
  • Software verification (program analysis, model checking, test generation)
  • Decision procedures for logical reasoning (SAT solvers, SMT solvers)

Elad Hazan, Room 409  

  • Research interests: machine learning methods and algorithms, efficient methods for mathematical optimization, regret minimization in games, reinforcement learning, control theory and practice
  • Machine learning, efficient methods for mathematical optimization, statistical and computational learning theory, regret minimization in games.
  • Implementation and algorithm engineering for control, reinforcement learning and robotics
  • Implementation and algorithm engineering for time series prediction

Felix Heide, Room 410

  • Research Areas: Computational Imaging, Computer Vision, Machine Learning (focus on Optimization and Approximate Inference).
  • Optical Neural Networks
  • Hardware-in-the-loop Holography
  • Zero-shot and Simulation-only Learning
  • Object recognition in extreme conditions
  • 3D Scene Representations for View Generation and Inverse Problems
  • Long-range Imaging in Scattering Media
  • Hardware-in-the-loop Illumination and Sensor Optimization
  • Inverse Lidar Design
  • Phase Retrieval Algorithms
  • Proximal Algorithms for Learning and Inference
  • Domain-Specific Language for Optics Design

Peter Henderson , 302 Sherrerd Hall

  • Research Areas: Machine learning, law, and policy

Kyle Jamieson, Room 306

  • Research areas: Wireless and mobile networking; indoor radar and indoor localization; Internet of Things
  • See other topics on my independent work  ideas page  (campus IP and CS dept. login req'd)

Alan Kaplan, 221 Nassau Street, Room 105

Research Areas:

  • Random apps of kindness - mobile application/technology frameworks used to help individuals or communities; topic areas include, but are not limited to: first response, accessibility, environment, sustainability, social activism, civic computing, tele-health, remote learning, crowdsourcing, etc.
  • Tools automating programming language interoperability - Java/C++, React Native/Java, etc.
  • Software visualization tools for education
  • Connected consumer devices, applications and protocols

Brian Kernighan, Room 311

  • Research Areas: application-specific languages, document preparation, user interfaces, software tools, programming methodology
  • Application-oriented languages, scripting languages.
  • Tools; user interfaces
  • Digital humanities

Zachary Kincaid, Room 219

  • Research areas: programming languages, program analysis, program verification, automated reasoning
  • Independent Research Topics:
  • Develop a practical algorithm for an intractable problem (e.g., by developing practical search heuristics, or by reducing to, or by identifying a tractable sub-problem, ...).
  • Design a domain-specific programming language, or prototype a new feature for an existing language.
  • Any interesting project related to programming languages or logic.

Gillat Kol, Room 316

  • Research area: theory

Aleksandra Korolova, 309 Sherrerd Hall

  • Research areas: Societal impacts of algorithms and AI; privacy; fair and privacy-preserving machine learning; algorithm auditing.

Advisees typically have taken one or more of COS 226, COS 324, COS 423, COS 424 or COS 445.

Pravesh Kothari, Room 320

  • Research areas: Theory

Amit Levy, Room 307

  • Research Areas: Operating Systems, Distributed Systems, Embedded Systems, Internet of Things
  • Distributed hardware testing infrastructure
  • Second factor security tokens
  • Low-power wireless network protocol implementation
  • USB device driver implementation

Kai Li, Room 321

  • Research Areas: Distributed systems; storage systems; content-based search and data analysis of large datasets.
  • Fast communication mechanisms for heterogeneous clusters.
  • Approximate nearest-neighbor search for high dimensional data.
  • Data analysis and prediction of in-patient medical data.
  • Optimized implementation of classification algorithms on manycore processors.

Xiaoyan Li, 221 Nassau Street, Room 104

  • Research areas: Information retrieval, novelty detection, question answering, AI, machine learning and data analysis.
  • Explore new statistical retrieval models for document retrieval and question answering.
  • Apply AI in various fields.
  • Apply supervised or unsupervised learning in health, education, finance, and social networks, etc.
  • Any interesting project related to AI, machine learning, and data analysis.

Lydia Liu, Room 414

  • Research Areas: algorithmic decision making, machine learning and society
  • Theoretical foundations for algorithmic decision making (e.g. mathematical modeling of data-driven decision processes, societal level dynamics)
  • Societal impacts of algorithms and AI through a socio-technical lens (e.g. normative implications of worst case ML metrics, prediction and model arbitrariness)
  • Machine learning for social impact domains, especially education (e.g. responsible development and use of LLMs for education equity and access)
  • Evaluation of human-AI decision making using statistical methods (e.g. causal inference of long term impact)

Wyatt Lloyd, Room 323

  • Research areas: Distributed Systems
  • Caching algorithms and implementations
  • Storage systems
  • Distributed transaction algorithms and implementations

Alex Lombardi , Room 312

  • Research Areas: Theory

Margaret Martonosi, Room 208

  • Quantum Computing research, particularly related to architecture and compiler issues for QC.
  • Computer architectures specialized for modern workloads (e.g., graph analytics, machine learning algorithms, mobile applications
  • Investigating security and privacy vulnerabilities in computer systems, particularly IoT devices.
  • Other topics in computer architecture or mobile / IoT systems also possible.

Jonathan Mayer, Sherrerd Hall, Room 307 

Available for Spring 2025 single-semester IW, only

  • Research areas: Technology law and policy, with emphasis on national security, criminal procedure, consumer privacy, network management, and online speech.
  • Assessing the effects of government policies, both in the public and private sectors.
  • Collecting new data that relates to government decision making, including surveying current business practices and studying user behavior.
  • Developing new tools to improve government processes and offer policy alternatives.

Mae Milano, Room 307

  • Local-first / peer-to-peer systems
  • Wide-ares storage systems
  • Consistency and protocol design
  • Type-safe concurrency
  • Language design
  • Gradual typing
  • Domain-specific languages
  • Languages for distributed systems

Andrés Monroy-Hernández, Room 405

  • Research Areas: Human-Computer Interaction, Social Computing, Public-Interest Technology, Augmented Reality, Urban Computing
  • Research interests:developing public-interest socio-technical systems.  We are currently creating alternatives to gig work platforms that are more equitable for all stakeholders. For instance, we are investigating the socio-technical affordances necessary to support a co-op food delivery network owned and managed by workers and restaurants. We are exploring novel system designs that support self-governance, decentralized/federated models, community-centered data ownership, and portable reputation systems.  We have opportunities for students interested in human-centered computing, UI/UX design, full-stack software development, and qualitative/quantitative user research.
  • Beyond our core projects, we are open to working on research projects that explore the use of emerging technologies, such as AR, wearables, NFTs, and DAOs, for creative and out-of-the-box applications.

Christopher Moretti, Corwin Hall, Room 036

  • Research areas: Distributed systems, high-throughput computing, computer science/engineering education
  • Expansion, improvement, and evaluation of open-source distributed computing software.
  • Applications of distributed computing for "big science" (e.g. biometrics, data mining, bioinformatics)
  • Software and best practices for computer science education and study, especially Princeton's 126/217/226 sequence or MOOCs development
  • Sports analytics and/or crowd-sourced computing

Radhika Nagpal, F316 Engineering Quadrangle

  • Research areas: control, robotics and dynamical systems

Karthik Narasimhan, Room 422

  • Research areas: Natural Language Processing, Reinforcement Learning
  • Autonomous agents for text-based games ( )
  • Transfer learning/generalization in NLP
  • Techniques for generating natural language
  • Model-based reinforcement learning

Arvind Narayanan, 308 Sherrerd Hall 

Research Areas: fair machine learning (and AI ethics more broadly), the social impact of algorithmic systems, tech policy

Pedro Paredes, Corwin Hall, Room 041

My primary research work is in Theoretical Computer Science.

 * Research Interest: Spectral Graph theory, Pseudorandomness, Complexity theory, Coding Theory, Quantum Information Theory, Combinatorics.

The IW projects I am interested in advising can be divided into three categories:

 1. Theoretical research

I am open to advise work on research projects in any topic in one of my research areas of interest. A project could also be based on writing a survey given results from a few papers. Students should have a solid background in math (e.g., elementary combinatorics, graph theory, discrete probability, basic algebra/calculus) and theoretical computer science (226 and 240 material, like big-O/Omega/Theta, basic complexity theory, basic fundamental algorithms). Mathematical maturity is a must.

A (non exhaustive) list of topics of projects I'm interested in:   * Explicit constructions of better vertex expanders and/or unique neighbor expanders.   * Construction deterministic or random high dimensional expanders.   * Pseudorandom generators for different problems.   * Topics around the quantum PCP conjecture.   * Topics around quantum error correcting codes and locally testable codes, including constructions, encoding and decoding algorithms.

 2. Theory informed practical implementations of algorithms   Very often the great advances in theoretical research are either not tested in practice or not even feasible to be implemented in practice. Thus, I am interested in any project that consists in trying to make theoretical ideas applicable in practice. This includes coming up with new algorithms that trade some theoretical guarantees for feasible implementation yet trying to retain the soul of the original idea; implementing new algorithms in a suitable programming language; and empirically testing practical implementations and comparing them with benchmarks / theoretical expectations. A project in this area doesn't have to be in my main areas of research, any theoretical result could be suitable for such a project.

Some examples of areas of interest:   * Streaming algorithms.   * Numeric linear algebra.   * Property testing.   * Parallel / Distributed algorithms.   * Online algorithms.    3. Machine learning with a theoretical foundation

I am interested in projects in machine learning that have some mathematical/theoretical, even if most of the project is applied. This includes topics like mathematical optimization, statistical learning, fairness and privacy.

One particular area I have been recently interested in is in the area of rating systems (e.g., Chess elo) and applications of this to experts problems.

Final Note: I am also willing to advise any project with any mathematical/theoretical component, even if it's not the main one; please reach out via email to chat about project ideas.

Iasonas Petras, Corwin Hall, Room 033

  • Research Areas: Information Based Complexity, Numerical Analysis, Quantum Computation.
  • Prerequisites: Reasonable mathematical maturity. In case of a project related to Quantum Computation a certain familiarity with quantum mechanics is required (related courses: ELE 396/PHY 208).
  • Possible research topics include:

1.   Quantum algorithms and circuits:

  • i. Design or simulation quantum circuits implementing quantum algorithms.
  • ii. Design of quantum algorithms solving/approximating continuous problems (such as Eigenvalue problems for Partial Differential Equations).

2.   Information Based Complexity:

  • i. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems in various settings (for example worst case or average case). 
  • ii. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems under new tractability and error criteria.
  • iii. Necessary and sufficient conditions for tractability of Weighted problems.
  • iv. Necessary and sufficient conditions for tractability of Weighted Problems under new tractability and error criteria.

3. Topics in Scientific Computation:

  • i. Randomness, Pseudorandomness, MC and QMC methods and their applications (Finance, etc)

Yuri Pritykin, 245 Carl Icahn Lab

  • Research interests: Computational biology; Cancer immunology; Regulation of gene expression; Functional genomics; Single-cell technologies.
  • Potential research projects: Development, implementation, assessment and/or application of algorithms for analysis, integration, interpretation and visualization of multi-dimensional data in molecular biology, particularly single-cell and spatial genomics data.

Benjamin Raphael, Room 309  

  • Research interests: Computational biology and bioinformatics; Cancer genomics; Algorithms and machine learning approaches for analysis of large-scale datasets
  • Implementation and application of algorithms to infer evolutionary processes in cancer
  • Identifying correlations between combinations of genomic mutations in human and cancer genomes
  • Design and implementation of algorithms for genome sequencing from new DNA sequencing technologies
  • Graph clustering and network anomaly detection, particularly using diffusion processes and methods from spectral graph theory

Vikram Ramaswamy, 035 Corwin Hall

  • Research areas: Interpretability of AI systems, Fairness in AI systems, Computer vision.
  • Constructing a new method to explain a model / create an interpretable by design model
  • Analyzing a current model / dataset to understand bias within the model/dataset
  • Proposing new fairness evaluations
  • Proposing new methods to train to improve fairness
  • Developing synthetic datasets for fairness / interpretability benchmarks
  • Understanding robustness of models

Ran Raz, Room 240

  • Research Area: Computational Complexity
  • Independent Research Topics: Computational Complexity, Information Theory, Quantum Computation, Theoretical Computer Science

Szymon Rusinkiewicz, Room 406

  • Research Areas: computer graphics; computer vision; 3D scanning; 3D printing; robotics; documentation and visualization of cultural heritage artifacts
  • Research ways of incorporating rotation invariance into computer visiontasks such as feature matching and classification
  • Investigate approaches to robust 3D scan matching
  • Model and compensate for imperfections in 3D printing
  • Given a collection of small mobile robots, apply control policies learned in simulation to the real robots.

Olga Russakovsky, Room 408

  • Research Areas: computer vision, machine learning, deep learning, crowdsourcing, fairness&bias in AI
  • Design a semantic segmentation deep learning model that can operate in a zero-shot setting (i.e., recognize and segment objects not seen during training)
  • Develop a deep learning classifier that is impervious to protected attributes (such as gender or race) that may be erroneously correlated with target classes
  • Build a computer vision system for the novel task of inferring what object (or part of an object) a human is referring to when pointing to a single pixel in the image. This includes both collecting an appropriate dataset using crowdsourcing on Amazon Mechanical Turk, creating a new deep learning formulation for this task, and running extensive analysis of both the data and the model

Sebastian Seung, Princeton Neuroscience Institute, Room 153

  • Research Areas: computational neuroscience, connectomics, "deep learning" neural networks, social computing, crowdsourcing, citizen science
  • Gamification of neuroscience (EyeWire  2.0)
  • Semantic segmentation and object detection in brain images from microscopy
  • Computational analysis of brain structure and function
  • Neural network theories of brain function

Jaswinder Pal Singh, Room 324

  • Research Areas: Boundary of technology and business/applications; building and scaling technology companies with special focus at that boundary; parallel computing systems and applications: parallel and distributed applications and their implications for software and architectural design; system software and programming environments for multiprocessors.
  • Develop a startup company idea, and build a plan/prototype for it.
  • Explore tradeoffs at the boundary of technology/product and business/applications in a chosen area.
  • Study and develop methods to infer insights from data in different application areas, from science to search to finance to others. 
  • Design and implement a parallel application. Possible areas include graphics, compression, biology, among many others. Analyze performance bottlenecks using existing tools, and compare programming models/languages.
  • Design and implement a scalable distributed algorithm.

Mona Singh, Room 420

  • Research Areas: computational molecular biology, as well as its interface with machine learning and algorithms.
  • Whole and cross-genome methods for predicting protein function and protein-protein interactions.
  • Analysis and prediction of biological networks.
  • Computational methods for inferring specific aspects of protein structure from protein sequence data.
  • Any other interesting project in computational molecular biology.

Robert Tarjan, 194 Nassau St., Room 308

  • Research Areas: Data structures; graph algorithms; combinatorial optimization; computational complexity; computational geometry; parallel algorithms.
  • Implement one or more data structures or combinatorial algorithms to provide insight into their empirical behavior.
  • Design and/or analyze various data structures and combinatorial algorithms.

Olga Troyanskaya, Room 320

  • Research Areas: Bioinformatics; analysis of large-scale biological data sets (genomics, gene expression, proteomics, biological networks); algorithms for integration of data from multiple data sources; visualization of biological data; machine learning methods in bioinformatics.
  • Implement and evaluate one or more gene expression analysis algorithm.
  • Develop algorithms for assessment of performance of genomic analysis methods.
  • Develop, implement, and evaluate visualization tools for heterogeneous biological data.

David Walker, Room 211

  • Research Areas: Programming languages, type systems, compilers, domain-specific languages, software-defined networking and security
  • Independent Research Topics:  Any other interesting project that involves humanitarian hacking, functional programming, domain-specific programming languages, type systems, compilers, software-defined networking, fault tolerance, language-based security, theorem proving, logic or logical frameworks.

Shengyi Wang, Postdoctoral Research Associate, Room 216

Available for Fall 2024 single-semester IW, only

  • Independent Research topics: Explore Escher-style tilings using (introductory) group theory and automata theory to produce beautiful pictures.

Kevin Wayne, Corwin Hall, Room 040

  • Research Areas: design, analysis, and implementation of algorithms; data structures; combinatorial optimization; graphs and networks.
  • Design and implement computer visualizations of algorithms or data structures.
  • Develop pedagogical tools or programming assignments for the computer science curriculum at Princeton and beyond.
  • Develop assessment infrastructure and assessments for MOOCs.

Matt Weinberg, 194 Nassau St., Room 222

  • Research Areas: algorithms, algorithmic game theory, mechanism design, game theoretical problems in {Bitcoin, networking, healthcare}.
  • Theoretical questions related to COS 445 topics such as matching theory, voting theory, auction design, etc. 
  • Theoretical questions related to incentives in applications like Bitcoin, the Internet, health care, etc. In a little bit more detail: protocols for these systems are often designed assuming that users will follow them. But often, users will actually be strictly happier to deviate from the intended protocol. How should we reason about user behavior in these protocols? How should we design protocols in these settings?

Huacheng Yu, Room 310

  • data structures
  • streaming algorithms
  • design and analyze data structures / streaming algorithms
  • prove impossibility results (lower bounds)
  • implement and evaluate data structures / streaming algorithms

Ellen Zhong, Room 314

Opportunities outside the department.

We encourage students to look in to doing interdisciplinary computer science research and to work with professors in departments other than computer science.  However, every CS independent work project must have a strong computer science element (even if it has other scientific or artistic elements as well.)  To do a project with an adviser outside of computer science you must have permission of the department.  This can be accomplished by having a second co-adviser within the computer science department or by contacting the independent work supervisor about the project and having he or she sign the independent work proposal form.

Here is a list of professors outside the computer science department who are eager to work with computer science undergraduates.

Maria Apostolaki, Engineering Quadrangle, C330

  • Research areas: Computing & Networking, Data & Information Science, Security & Privacy

Branko Glisic, Engineering Quadrangle, Room E330

  • Documentation of historic structures
  • Cyber physical systems for structural health monitoring
  • Developing virtual and augmented reality applications for documenting structures
  • Applying machine learning techniques to generate 3D models from 2D plans of buildings
  •  Contact : Rebecca Napolitano, rkn2 (

Mihir Kshirsagar, Sherrerd Hall, Room 315

Center for Information Technology Policy.

  • Consumer protection
  • Content regulation
  • Competition law
  • Economic development
  • Surveillance and discrimination

Sharad Malik, Engineering Quadrangle, Room B224

Select a Senior Thesis Adviser for the 2020-21 Academic Year.

  • Design of reliable hardware systems
  • Verifying complex software and hardware systems

Prateek Mittal, Engineering Quadrangle, Room B236

  • Internet security and privacy 
  • Social Networks
  • Privacy technologies, anonymous communication
  • Network Science
  • Internet security and privacy: The insecurity of Internet protocols and services threatens the safety of our critical network infrastructure and billions of end users. How can we defend end users as well as our critical network infrastructure from attacks?
  • Trustworthy social systems: Online social networks (OSNs) such as Facebook, Google+, and Twitter have revolutionized the way our society communicates. How can we leverage social connections between users to design the next generation of communication systems?
  • Privacy Technologies: Privacy on the Internet is eroding rapidly, with businesses and governments mining sensitive user information. How can we protect the privacy of our online communications? The Tor project ( is a potential application of interest.

Ken Norman,  Psychology Dept, PNI 137

  • Research Areas: Memory, the brain and computation 
  • Lab:  Princeton Computational Memory Lab

Potential research topics

  • Methods for decoding cognitive state information from neuroimaging data (fMRI and EEG) 
  • Neural network simulations of learning and memory

Caroline Savage

Office of Sustainability, Phone:(609)258-7513, Email: cs35 (

The  Campus as Lab  program supports students using the Princeton campus as a living laboratory to solve sustainability challenges. The Office of Sustainability has created a list of campus as lab research questions, filterable by discipline and topic, on its  website .

An example from Computer Science could include using  TigerEnergy , a platform which provides real-time data on campus energy generation and consumption, to study one of the many energy systems or buildings on campus. Three CS students used TigerEnergy to create a  live energy heatmap of campus .

Other potential projects include:

  • Apply game theory to sustainability challenges
  • Develop a tool to help visualize interactions between complex campus systems, e.g. energy and water use, transportation and storm water runoff, purchasing and waste, etc.
  • How can we learn (in aggregate) about individuals’ waste, energy, transportation, and other behaviors without impinging on privacy?

Janet Vertesi, Sociology Dept, Wallace Hall, Room 122

  • Research areas: Sociology of technology; Human-computer interaction; Ubiquitous computing.
  • Possible projects: At the intersection of computer science and social science, my students have built mixed reality games, produced artistic and interactive installations, and studied mixed human-robot teams, among other projects.

David Wentzlaff, Engineering Quadrangle, Room 228

Computing, Operating Systems, Sustainable Computing.

  • Instrument Princeton's Green (HPCRC) data center
  • Investigate power utilization on an processor core implemented in an FPGA
  • Dismantle and document all of the components in modern electronics. Invent new ways to build computers that can be recycled easier.
  • Other topics in parallel computer architecture or operating systems



Student Projects and Thesis Topics

Selection of proposals for student projects ("Projekt" for Bachelor, "Praktikum" and "Team-Projekt" for Master) and thesis topics (Bachelor and Master). Please do not hesitate to contact us if you are interested in a project or thesis at the Chair of Software Engineering. If you have your own idea for a project or a thesis topic: Let's talk about it!

Available - Read More…

In progress

Selection of student projects and thesis topics on which students are currently working on. If you find one of the topics interesting please ask the tutor about similar or follow up projects/theses.

In progress - Read More…

Selection of student projects and thesis topics that have already been finished. If you find one of the topics interesting please ask the tutor about similar or follow up projects/theses.

Finished - Read More…

Topics for Theses and Projects

Our topics for bachelor and master theses as well as projects are from the areas of software engineering and programming languages. The concrete topics for theses are based on our research interests  and allow students to make their own contribution to a field of research. Our main target group are students of Computer Science, Software Engineering, Media Informatics, Artificial Intelligence, and Cognitive Systems.

This page offers a selection of topics and subject areas. For more information, please do not hesitate to contact the respective supervisor. In addition, we are open to your own suggestions for topics.

(Legend - B: Bachelor Thesis, M: Master Thesis, P: Project)

Outline of a Bachelor or Master Thesis

Topic and proposal.

After the initial contact, the topic and the contents of the Bachelor or Master thesis are agreed upon and recorded in a proposal. The proposal has proven to be a valueable tool for risk minimization and planning of the thesis and includes:

  • the context of the thesis
  • the research question
  • the state of research
  • the solution idea
  • the methodology and the evaluation plan

Much of the proposal can be reused in the final thesis.

Interim Presentation

In the course of the interim presentation, students learn to present and communicate results. In addition, the interim presentation can be used to reflect on the status of the thesis so far and to respond to feedback.

Submission and Final Presentation

The submission of the final thesis and the final presentation formally conclude the bachelor or master thesis.

(Legend - B: Bachelor thesis, M: Master thesis, P: Project)

Human-centered Software Engineering

Software variability and evolution, constraint handling rules, dynamic and static program analysis, p/b/m: static analysis for reflective or self modifying code (sihler, tichy).

P/B/M: Static Analysis for Reflective or Self Modifying Code (Sihler, Tichy)

Context Most static analyzers rely on static dataflow analysis to detect problems like possible null pointer exceptions in code [5]. However, analyzers are usually unable to handle reflective or self-modifying code (e.g., Java Agents , Java Reflection , R's meta-functions [6]. While this is fine for languages in which such constructs are rare or discouraged, they are 1) used quite often in the R programming language and 2) pose an interesting problem to solve.

Problem As a basis [3], I have previously created the static dataflow analyzer and program slicer flowR for the R programming language. However, it is currently unable to deal with these reflective and code-modifying constructs like eval , body , quote , and parse in its static dataflow graph. While handling such constructs statically may be infeasible in the general case, we first want to focus on a set of common cases that appear frequently.

  • Develop a concept to represent code-modifications and lazy evaluation (within flowR 's dataflow graph). For example, to represent a function that has the default values of its arguments or the contents of its body modified.
  • Create a proof of concept implementation for this concept in flowR .

Related Work and Further Reading

  • K. Cooper and L Torczon. Engineering a Compiler. ( ISBN : 978-0-12-818926-9)
  • U. Khedker, A. Sanyal, and B. Sathe. Data Flow Analysis: Theory and Practice. ( ISBN : 978-0-8493-3251-7)
  • F. Sihler. Constructing a Static Program Slicer for R Programs.
  • A. Ko and B. Myers. Finding causes of program output with the Java Whyline.
  • SonarQube, Sonar.
  • Anckaert, B., Madou, M., De Bosschere, K. A Model for Self-Modifying Code.

If you want to, you can have a first look at flowR for yourself: .

Contact and More If you are interested and/or have any questions, feel free to contact me any time. We can discuss the topic further and try to adapt it to your personal preferences. Florian Sihler

P/B/M: Dynamic Dataflow Analysis for R Programs (Sihler, Tichy)

P/B/M: Dynamic Dataflow Analysis for R Programs (Sihler, Tichy)

Dataflow analysis is a very useful and important technique, used, for example, as part of compiler optimizations [1,2] and program comprehension techniques (e.g., slicing [3] or debugging [4]).

Although there is no single dataflow analysis (each analysis answers a slightly different question), dataflow analyzers usually identify how variables in a program relate to each other (e.g., which definitions a variable read my refer to).

Dataflow Analyzers can be split into:

  • static analyzers if they use only the source code of a program as input, and
  • dynamic analyzers if they use a specific program execution as input.

While static analysis is usually harder, it has lower application constraints as 1) it does not require inputs (from users, files, network-messages, ...), and 2) we do not have to deal with getting a potentially unknown program running. However, dynamic analyzers are usually much more valuable during debugging as they know the path the program took, the potential user inputs, the contents of external files, and more.

Within my master's thesis [3] that is now the basis of my PhD, I have created the static program slicer flowR for the R programming language, which includes a static dataflow analyzer. However, it offers no dynamic dataflow analysis and does not even attempt to run the respective input program.

  • Enrich flowR 's existing pipeline of parsing , normalizing , static dataflow extraction , static slicing , and code reconstruction with a dynamic dataflow analysis step.
  • Given a program (for starters without any external dependencies), the dynamic analysis should be able to determine the execution trace of the program (e.g., branches taken, loops entered and iteration requiered) with the help of R's debugging capabilities and active bindings [5].
  • From that, it should be able to infer which variable references read which values (e.g., which definition of a variable was read), what functions have been called, ...
  • The planned evaluation is to compare the results of the dynamic analysis with the results of the static analysis and to determine the differences.
  • R, Active Bindings

Contact and More

If you are interested and/or have any questions, feel free to contact me any time. We can discuss the topic further and try to adapt it to your personal preferences. Florian Sihler

P/M: Can ChatGPT Be Used as a Linter? (Sihler, Tichy)

P/M: Can ChatGPT Be Used as a Linter? (Sihler, Tichy)

Static Program Analysis is a well-researched field [1,2], useful in various domains like compiler optimizations [3] and linting [4]. However, static analysis is unable to find semantic smells and bugs and requires a lot of work to set up. On the other hand, current large language models (LLMs, like ChatGPT) can quickly answer questions about code and find (potential) semantic and syntactic bugs, with an easy-to-use interface and setup required.

Even though LLMs are easy to use and quick to give an answer, this answer is not always correct [5]. Furthermore, with their hype being relatively new, there is not much research on how their hallucinations hinder linting tasks or make them outright harmful. To address that, we want to analyze common smells and errors in real-world code (including those that common linters can not find), synthetically generate code with these smells and errors, and then analyze how well LLMs can detect as well as "fix" them.

  • Identify common smells and errors in real-world R code.
  • Synthetically generate code with these smells and errors.
  • Analyze/Classify how well LLMs can detect and fix those problems.
  • García-Ferreira et al., Static analysis: a brief survey, 2016
  • Anjana Gosain et al., Static Analysis: A Survey of Techniques and Tools, 2015
  • Hester et al., lintr: A 'Linter' for R Code, 2023
  • Zhang et al., Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models, 2023

[RESERVED] P/B/M: Pointer Analysis for Static Dataflow (Sihler, Tichy)

[RESERVED] P/B/M: Pointer Analysis for Static Dataflow (Sihler, Tichy)

Context Dataflow analysis is a very useful and important technique, used, for example, to

  • allow compiler optimizations [1,2],
  • to aide program comprehension (e.g.,  [3] or debugging [4]), and
  • perform code analysis (e.g., to locate possible null pointer exceptions [5]).

A static dataflow analyzer takes the source code of a program as its input and identifies how variables in a program relate to each other (e.g., which definitions a variable read my refer to).

However, this can happen on arbitrary granularity levels. For example, when reading a single cell of an array, a coarsely grained analyzer may refer to any potential write to the array, while a more detailed analysis could restrict the definitions to those that modify the respective entry.

Problem Within my master's thesis [3], which is now the basis of my PhD, I have created the static program slicer flowR for the R programming language, which includes a static dataflow analyzer. However, it does currently not differentiate the individual cells of arrays or the attributes of an object (i.e., it does not analyze pointers) [6].

  • Differentiate Cells of a Vector with constant access
  • Differentiate Data-Frames, Slots, and other pointer-types
  • Track Aliases to identify when pointers relate to each other
  • Evaluate the achieved reduction in the size of the resulting slices
  • M. Hind. Pointer Analysis: Haven’t We Solved This Problem Yet?

Relaxed Conformance Editing

M: freedom vs. restrictions: where is the sweet spot of graphical modeling tools.

Graphical modeling is a widely used task in software and systems engineering. Similar to how an IDE assists with programming, graphical modeling tools are intended to help create models as efficiently as possible. Basically, two types of graphical modeling tools can be distinguished: Either the tool is tailored to a graphical language and restricts the user in drawing in such a way that only syntactically correct models can be drawn (correct-by-constructionn approach), or one has a pure drawing tool that offers no language-specific support but allows maximum freedom. As so often, the optimum lies somewhere in between.

In this master thesis, a prototypical implementation of a graphical modeling tool in React is to be adapted in such a way that it is possible to turn various support mechanisms (and also restrictions) on or off. A user study will then be conducted with this customizable tool, with the goal of finding the sweet spot between maximum restriction and maximum freedom. In order not to make the study too large, only a few useful combinations will be compared.

  • Familiarization with the corresponding tool
  • Development and implementation of meaningful support or restriction options
  • Conducting a user study (study design, creating user tasks, implementation, evaluation)

Needed skills

  • Javascript/Typescript knowledge
  • Experiences with React
  • Interest in usability and study design

Further reading

  • Master thesis Leander Nachreiner

Alexander Raschke

Self-adaptive Systems

P: develop visualization for self-adaptive cloud systems (straub, tichy).

Self-Adaptive systems are systems that adjust themselves to maintain or improve their performance in response to changes in their environment and operational conditions, thereby ensuring continued effectiveness, reliability, and efficiency. Self-adaptive systems are diverse and multifaceted, with applications extending across numerous fields. In our project, we concentrate on the cloud-native domain, with a special emphasis on the explainability aspect of self-adaptation. This involves delving into how these systems can not only adjust autonomously to changing conditions but also provide transparent and understandable explanations for their adaptations, ensuring clarity and trust in their operations.

Understanding the intricacies of self-adaptive systems, particularly in the cloud-native space, is a complex task. The autonomous adjustments these systems make in response to environmental changes can be intricate and opaque. This complexity underscores the necessity for effective visualization strategies. Visualizations can range from simple schematic diagrams that illustrate system workflows, to advanced interactive visualizations that provide real-time insights into system dynamics. By employing visualization techniques like this, we aim to make the processes of self-adaptation in cloud-native systems not only more transparent but also more accessible to a broader audience, enhancing comprehension and facilitating informed oversight.

In this project you will integrate such an visualization approch. The implementation language is Typescript.

P: Develop Visualization for Self-Adaptive Cloud Systems (Straub, Tichy)

M: Data-Queries for Basic Explanations by LLMs (Straub, Tichy)

Self-adaptive systems represent a significant leap in technology. These systems are capable of adjusting their behavior in response to changes in their environment or in their own state. This adaptability makes them incredibly powerful, yet also complex. Large Language Models (LLMs) have shown remarkable proficiency in generating human-like text, offering potential as tools for simplifying and explaining complex technical concepts We plan to use the capabilities of LLMs to explain these complex self-adaptive systems. However, a significant challenge arises: how can these LLMs access detailed and up-to-date information about the self-adaptive systems they are explaining?

In this Master thesis, the different possibilities of enabeling the LLM to access the required data need to be explored. An example would be Retrieval Augmented Generation (RAG), which is already implemented in Libraries like LangChain. A proptotype implementation has to be created and connected with the MENTOR project. Finally, the appraoch has to be evaluted.


  • Familiarization with the possible approaches
  • Implement a Prototype
  • Evaluate the Implementation

Software Configuration

Formal languages for variability, p/b/m: survey on sharing metrics for formal variability languages (bittner, thüm).

P/B/M: Survey on Sharing Metrics for Formal Variability Languages (Bittner, Thüm)

When it comes to developing multi-variant software systems, software product-line engineering and analyses avoid duplicate computational effort by exploiting similarities between the different software variants. For example, in the above example the statement "lol;" is shared between the software variants including or excluding feature A . To describe and analyze variability, formal languages have been proposed that allow semantic-preserving translations to refactor expressions to increase sharing. However, the notion of having "more sharing" in a formula remains vague most of the time or different metrics have been used in the literature to measure sharing.

  • Literature survey on sharing metrics (for formal variability languages)
  • Qualitative comparison between sharing metrics
  • Definition of new metrics if necessary
  • Perhaps empirical evaluation of different metrics for real systems

Related Work

  • The Choice Calculus

A Formal Framework of Software Product Line Analyses

Paul Bittner

Thomas Thüm

Constraint-Programmierung und Constraint Handling Rules

P/b/m: graph tool for mason marks (frühwirth).

We are developing a rule-based implementation of a tool to analyse and generate graphs. It is used in the domain of mason’s marks. For thousands of years, stonemasons have been inscribing these symbolic signs on dressed stone. Geometrically, mason’s marks are line drawings. They consist of a pattern of straight lines, sometimes circles and arcs. We represent mason’s marks by connected planar graphs. Our prototype tool for analysis and generation of graphs is written in the rule-based declarative language Constraint Handling Rules. One or several of following features could be improved in this proposed work:


  • improve the vertex-centric logical graph representation, i.e. adding arcs, colors, labels,
  • encode existing mason marks either by hand, from images or import from existing databases,
  • recognize (sub)graphs and patterns in a graph, in particular (self-)similarities and correlations between graphs,
  • perform classical algorithms on graphs like shortest paths,
  • derive properties and statistics from graphs,
  • generate (randomly or exhaustively) and draw graphs from given constrained subgraphs based on properties and statistics.
  • Thom Frühwirth: Rule-Based Drawing, Analysis and Generation of Graphs for Mason's Mark Design, 2018


  • Good knowledge of Prolog and CHR
  • Lecture Rule-based Programming

Thom Frühwirth , Sascha Rechenberger

P/B/M: Graph Tool for Mason Marks (Frühwirth)

P/B/M: Justifications in CHR for Logical Retraction in Dynamic Algorithms (Frühwirth)

When algorithms are written in CHR, constraints represent both data and operations. CHR is already incremental by nature, i.e. constraints can be added at runtime. Logical retraction adds decrementality. Hence any algorithm written in CHR with justifications will become fully dynamic. Operations can be undone and data can be removed at any point in the computation without compromising the correctness of the result.

A straightforward source-to-source transformation can introduce justifications for user-defined constraints into the CHR. Then a scheme of two rules suffices to allow for logical retraction (deletion, removal) of constraints during computation. Without the need to recompute from scratch, these rules remove not only the constraint but also undo all consequences of the rule applications that involved the constraint.

Further work should investigate implementation, dynamic algorithms and application domains of CHR with justifications:

  • research how logical as well as classical algorithms implemented in CHR behave when they become dynamic.
  • improve the implementation, optimize and benchmark it.
  • support detection and repair of inconsistencies (for error diagnosis), - support nonmonotonic logical behaviors (e.g. default logic, abduction, defeasible reasoning).
  • Thom Frühwirth: Justifications in Constraint Handling Rules for Logical Retraction in Dynamic Algorithms.
  • CHR translator


  • Interest to learn about formal analysis methods of rule-based languages

P/B/M: Justifications in CHR for Logical Retraction in Dynamic Algorithms (Frühwirth)

B/M: Non-Termination Analysis of Recursive Rules (Frühwirth)

Extend the analysis techniques and/or the associated tool from the following two research papers:

A dynamic program analysis of the non-termination problem for recursion in the Constraint Handling Rules (CHR) language: A simple program transformation for recursive rules in CHR was introduced that produces one or more adversary rules. When the rules are executed together, a non-terminating computation may arise. It was shown that any non-terminating computation of the original rule contains this witness computation.

  • Thom Fruehwirth: A Devil's Advocate against Termination of Direct Recursion, PPDP 2015.
  • Transformation Tool available (use "Devil" options).

A static program analysis of the non-termination problem for recursion in the Constraint Handling Rules (CHR) language: Theorems with so-called misbehavior conditions for potential non-termination and failure (as well as definite termination) of linear direct recursive simplification rules are given. Logical relationships between the constraints in a recursive rule play a crucial role in this kind of program analysis.

  • Thom Fruehwirth: Why Can’t You Behave? Non-Termination Analysis of Direct Recursive Rules with Constraints, RuleML 2016
  • Lecture "Rule-Based Programming"

B/M: Non-Termination Analysis of Recursive Rules (Frühwirth)

P/B/M: Localized Constraint Stores (Frühwirth)

In distributed computation, data and processes are distributed over a network of stores and processing units. In a constraint-based programming language paradigm this means that constraints have to be annotated with spatial information defining their whereabouts. Obvious topologies are a distinction between global and local stores as well as trees. Localized constraints can also be used for so-called reified (or meta-)constraints (e.g. ), to store justifications and for spatial reasoning.

In Constraint Handling Rules (CHR), there is a simple source-to-source program transformation that adds local annotations to constraints.

The scope of the work includes implementation of such a transformation, their application and/or static program analysis to derive distribution patterns, i.e. to localize constraint computation while minimizing communication overhead.

  • Edmund S. L. Lam, Iliano Cervesato and Nabeeha Fatima: CoMingle: Distributed Logic Programming for Decentralized Mobile Ensembles. In proceedings of International Conference on Distributed Computing Techniques (Coordination'15)
  • A. Raffaeta and T. Frühwirth: Spatio-Temporal Annotated Constraint Logic Programming, Third International Symposium on Practical Aspects of Declarative Languages (PADL'01), Las Vegas, USA, March 2001.
  • T. Frühwirth: Entailment Simplification and Constraint Constructors for User-Defined Constraints, Third Workshop on Constraint Logic Programming (WCLP 93), Marseille, France, March 1993.

P/B/M: Localized Constraint Stores (Frühwirth)

P/B/M: Invariant Checking and Generation by Confluence and Completion (Frühwirth)

Invariants (or assertions, properties, conditions) annotate program text and express static and dynamic properties of a program's execution. Invariants can be expressed as logical relations (predicates) over the program's variables. In the context of constraint-programming and Constraint Handling Rules (CHR), they amount to constraints. These can be readily added to the program to enforce the invariants. By comparing the program with and without invariants expressed as constraints using established program analysis techniques for CHR, namely confluence and program equivalence, we can check if the invariants hold in the program.

Furthermore, invariants can be strenghened and even be generated by adapting the so-called completion method (that is normally used to generate additional rules to make a CHR program confluent).

  • Johannes Langbein, Frank Raiser, Thom Frühwirth: A state equivalence and confluence checker for CHR. In P. Van Weert and L. De Koninck, editors, CHR '10: Proc. 7th Workshop on Constraint Handling Rules. K.U.Leuven, Department of Computer Science, Technical report CW 588, July 2010.
  • Lecture "Rule-based Programming"

B/M: Program Slicing by Confluence and Completion (Frühwirth)

Program slicing is a program anaylsis technique whereby one extracts properties and relationships of variables in a program by removing from the program all statements that do not effect the assignments of the variables. In the context of constraint programming and Constraint Handling Rules that deal with logical relations (predicates) this amounts to the logical operation of variable projection. This means that we remove unwanted variables that are not of interest from the program by program transformation. This transformation can be accomplished by adapting the technique of "completion". It is usually used to make a non-confluent program confluent.

  • Johannes Langbein, Frank Raiser, Thom Frühwirth. A state equivalence and confluence checker for CHR. In P. Van Weert and L. De Koninck, editors, CHR '10: Proc. 7th Workshop on Constraint Handling Rules. K.U.Leuven, Department of Computer Science, Technical report CW 588, July 2010.
  • Lecture "Rule-based Programming

B/M: Program Slicing by Confluence and Completion (Frühwirth)

M: Repeated Recursion Unfolding for Super-Linear Speedup (Frühwirth)

Repeated recursion unfolding is a new approach that repeatedly unfolds a recursion with itself and simplifies it while keeping all unfolded rules. Each unfolding doubles the number of recursive steps covered. This reduces the number of recursive rule applications to its logarithm at the expense of introducing a logarithmic number of unfolded rules to the program. Efficiency crucially depends on the amount of simplification inside the unfolded rules. A super-linear speedup is provably possible in the best case, i.e. speedup by more than a constant factor. The optimization can lower the time complexity class of a program.

The goal is to implement this optimization scheme as a program transformation in the programming language of choice. If necessary, the scheme should be transferred from recursion to iteration constructs such as loops.

  • Thom Frühwirth: Runtime Repeated Recursion Unfolding in CHR: A Just-In-Time Online Program Optimization Strategy That Can Achieve Super-Linear Speedup, 2023 (DOI: 10.48550/arXiv.2307.02180)


  • Excellent knowledge and programming skills in the choosen programming language.

M: CHR Abstract Machine (Rechenberger, Frühwirth)

Prolog (WAM) and then Java (JVM) popularized the concept of an abstract (or virtual) machine to implement programming languages in a systematic, portable yet efficient way. Such a machine shall be developed for CHR.

Define the abstract code instructions for CHR and to implement them.

  • Lecture Compiler Construction (Compilerbau) (optional but very helpful)

M: Structured Literature Research on CHR Implementations (Rechenberger, Frühwirth)

The declarative programming language Constraint Handling Rules (CHR) is designed as a language extension to other, not necessarily declarative programming languages. There are existing implementations for Prolog , C , Java , JavaScript , and others. We want to conduct a Structured Literature Research (SLR) on existing implementations, to get an exhaustive overview over implementation techniques and patterns.

  • Conduct an SLR on papers on existing CHR implementations
  • Find CHR implementations without a scientific publication on public repositories on, e.g. GitHub, GitLab, ...
  • Identify and document common architectures, implementation techniques and patterns
  • Get an exhaustive overview over existing and historic CHR implementations
  • T. Frühwirth: Constraint Handling Rules - What Else?
  • S. Sneyers et al.: As time goes by: Constraint Handling Rules
  • P. Van Weert: Efficient Lazy Evaluation of Rule-Based Programs
  • P. Van Weert, P. Wuille, T. Schrijvers, B. Demoen: CHR for Imperative Host Languages
  • F. Nogatz, T. Frühwirth, D. Seipel: CHR.js: A CHR Implementation in JavaScript
  • Dragan Ivanović: Implementing Constraint Handling Rules as a Domain-Specific Language Embedded in Java
  • Interest in programming languages and (to some extend) compiler construction.
  • Good knowledge of multiple programming languages and paradigms.

Sascha Rechenberger

B/M: Failure in FreeCHR (Rechenberger, Frühwirth)

FreeCHR aims to be a sound and complete embedding framework for CHR. Hence, we want to extend the operational semantics by possibly failing computation, as they are necessary for the development of constraint solvers and other software.

  • Extend the very abstract operational semantics of FreeCHR, such that they can model possibly failing computations
  • Prove soundness and completeness w.r.t. the v ery abstract operational semantics of CHR
  • Optional : Develop an execution algorithm and prove correctness w.r.t. the new operational semantics
  • S. Rechenberger, T. Frühwirth: FreeCHR - An Algebraic Framework for CHR Embeddings
  • T. Frühwirth: Constraint Handling Rules (ISBN: 978-0-521-87776-3 )
  • Interest in formal aspects of programming languages
  • Interest/knowledge in category theory and/or type systems is recommended
  • Knowledge in functional programming, especially monads

B/M: Stateful Computations in FreeCHR (Rechenberger, Frühwirth)

FreeCHR aims to be a sound and complete embedding framework for CHR. Hence, we want to extend the operational semantics by stateful computation, as they are common in many programming languages.

  • Extend the very abstract operational semantics of FreeCHR, such that they can model stateful computations

M: Abstract Operational Semantics for FreeCHR (Rechenberger, Frühwirth)

FreeCHR aims to be a sound and complete embedding framework for CHR. The abstract operational semantics are a next step in the direction of modelling the necessities of real-life programming languages. Hence, we want to re-formalize them in the context of FreeCHR and establish soundness and completeness.

  • Formalize the abstract operational semantics ω t of CHR in the terms of (side-effect free) FreeCHR
  • Prove soundness and completeness of the new definition

Offered MSc Thesis topics

See also our current list of projects on the Research page to get an idea of what is topical in our research. Another list of all our projects is also available in Tuhat, with responsible persons listed (you can ask them about potential thesis topics).

A more exhaustive list of topics from the department is available at CSM Master thesis topics (moodle).

General writing Instructions

We have written some instructions to help the students write their Master's theses, seminar papers and B.Sc. theses. Please, read the guide before starting your thesis work: Scientific Writing – Guide of the Empirical Software Engineering Research Group .

Master's Thesis Topics

Software engineering and technology are prevalent areas for thesis at the department, and many candidates ask for thesis topics every academic year. We do our best to accommodate the requests, but the applicants can smoothen the process by taking an active role in thinking about potential topics based on the themes presented below.

We provide guidance for selecting a suitable topic and the supervision and support needed to complete the work. Please contact Antti-Pekka Tuovinen or Tomi Männistö if you are interested. You can also contact the group members to ask about the subject areas they are working on.

Suppose you, as a student, are working in software development, processes, architecture or something related. In that case, there is a good chance of finding an interesting thesis topic that closely relates to your work. In such a case, the actual work often provides an excellent problem to investigate, propose or try out potential solutions for, or the case can act as a rich source of data about the practice of software development.

We also welcome companies to suggest potential topics for Master's thesis. The topics can be general, based on existing research, or they may require original research and problem-solving. We will help to evaluate and fine-tune the proposals. Depending on the topic, you may also need to be prepared to provide guidance and assistance during the thesis project.

Please contact Antti-Pekka Tuovinen or Tomi Männistö if you have an idea for an industrial thesis and need further information.

The listing below introduces our current research areas and potential topics for the thesis. Each topic has a short description and the names of the researchers working on the topic. Please contact them for more details about the research and thesis work. Note that you can also suggest and discuss other topics within the general area of software engineering research. We encourage creativity and student-centred insight in selecting and defining the topic.

Earlier theses

Some earlier MSc thesis titles below give some idea about the topics. You can try looking up more info from E-thesis , but note that it is up to the author if the actual thesis pdf is available online. Just search using the title (or part of it) in quotation marks. You can also go to the library in person and read all theses (even those without a public pdf) on a kiosk workstation (ask the staff if you need help).

  • Exploring study paths and study success in undergraduate Computer Science studies
  • EU:n tietosuoja-asetuksen GDPR:n vaikutus suomalaisissa pk-yrityksissä 2018-2020
  • Industrial Surveys on Software Testing Practices: A Literature Review
  • Laskennallisesti raskaan simulointiohjelmistokomponentin korvaaminen reaaliaikasovelluksessa koneoppimismenetelmällä
  • Web service monitoring tool development
  • Case study: identifying developer oriented features and capabilities of API developer portals
  • Documenting software architecture design decisions in continuous software development – a multivocal literature review
  • Elinikäinen oppiminen ohjelmistotuotannon ammattilaisen keskeisenä
  • Miten huoltovarmuus toteutuu Ylen verkkouutisissa?
  • Utilizing Clustering to Create New Industrial Classifications of Finnish Businesses: Design Science Approach
  • Smoke Testing Display Viewer 5
  • Modernizing usability and development with microservices
  • On the affect of psychological safety, team leader’s behaviour and team’s gender diversity on software team performance: A literature review
  • Lean software development and remote working during COVID-19 - a case study
  • Julkaisusyklin tihentämisen odotukset, haasteet ja ratkaisut
  • Software Development in the Fintech Industry: A Literature Review
  • Design of an automated pipeline to improve the process of cross-platform mobile building and deployment
  • Haasteet toimijamallin käytössä ohjelmistokehityksessä, systemaattinen kirjallisuuskatsaus
  • Light-weight method for detecting API breakages in microservice architectures
  • Kirjallisuuskatsaus ja tapaustutkimus API-hallinnasta mikropalveluarkkitehtuurissa
  • In-depth comparison of BDD testing frameworks for Java
  • Itseohjautuvan auton moraalikoneen kehittämisen haasteet
  • Towards secure software development at Neste - a case study
  • Etuuspohjaisen eläkejärjestelyn laskennan optimointi vakuutustenhallintajärjestelmässä
  • Internal software startup within a university – producing industry-ready graduates
  • Applying global software development approaches to building high-performing software teams
  • Systemaattinen kirjallisuuskatsaus lääkinnällisistä ohjelmistoista ja ketterästä ohjelmistokehityksestä
  • Matalan kynnyksen ohjelmointialustan hyödyntäminen projektinhalinnassa
  • Uncertainty Estimation with Calibrated Confidence Scores
  • Tool for grouping test log failures using string similarity algorithms
  • Design, Implementation, and Validation of a Uniform Control Interface for Drawing Robots with ROS2
  • Assuring Model Documentation in Continuous Machine Learning System Development
  • Verkkopalvelun saavutettavuuden arviointi ja kehittäminen ohjelmistotuoteyrityksessä
  • Methods for API Governance automation: managing interfaces in a microservice system
  • Improving Web Performance by Optimizing Cascading Style Sheets (CSS): Literature Review and Empirical Findings
  • Implementing continuous delivery for legacy software
  • Using ISO/IEC 29110 to Improve Software Testing in an Agile VSE
  • An Open-Source and Portable MLOps Pipeline for Continuous Training and Continuous Deployment
  • System-level testing with microservice architecture
  • Green in software engineering: tools, methods and practices for reducing the environmental impacts of software use – a literature review
  • Machine Learning Monitoring and Maintenance: A Multivocal Literature Review
  • Green in Software Engineering: A Systematic Literature Review
  • Comparison of Two Open Source Feature Stores for Explainable Machine Learning
  • Open-source tools for automatic generation of game content
  • Verkkosovelluskehysten energiankulutus: vertaileva tutkimus Blazor WebAssembly ja JavaScript
  • Infrastruktuuri koodina -toimintatavan tehostaminen
  • Geospatial DBSCAN Hyperparameter Optimization with a Novel Genetic Algorithm Method
  • Hybrid mobile development using Ionic framework
  • Correlation of Unit Test Code Coverage with Software Quality
  • Factors affecting productivity of software development teams and individual developers: A systematic literature review
  • Case study: Performance of JavaScript on server side
  • Reducing complexity of microservices with API-Saga
  • Organizing software architecture work in a multi-team, multi-project, agile environment
  • Cloud-based visual programming BIM design workflow
  • IT SIAM toimintojen kehitysprojekti
  • PhyloStreamer: A cloud focused application for integrating phylogenetic command-line tools into graphical interfaces
  • Evaluation of WebView Rendering Performance in the Context of React Native
  • A Thematic Review of Preventing Bias in Iterative AI Software Development
  • Adopting Machine Learning Pipeline in Existing Environment

Current topic areas of interest to the research group (see below for the details)

Open source-related topic areas in collaboration with daimler truck.

  • Open Chain: Developing the Journey to Open Chain Compliance at the example of Daimler Truck
  • How should an industrial company (for example, Daimler Truck) leverage open source software: Building a framework with different dimensions, from efficient governance to value in inner source and open source projects
  • How can an organization efficiently incentivize inner-source activities? (on different levels, culture, infrastructure, governance, regulations & commitments.)
  • How can an industrial organization leverage value from actively engaging in FOSS activities (especially on active creation and contribution)
  • How can spillovers help Industrial companies to educate the rare resources but also attract and retain talent? Ref: Gandal, N., Naftaliev, P., & Stettner, U. (2017). Following the code: spillovers and knowledge transfer. Review of Network Economics , 16 (3), 243-267. Abstract: Knowledge spillovers in Open Source Software (OSS) can occur via two channels: In the first channel, programmers take knowledge and experience gained from one OSS project they work on and employ it in another OSS project they work on. In the second channel, programmers reuse software code by taking code from an OSS project and employing it in another. We develop a methodology to measure software reuse in a large OSS network at the micro level and show that projects that reuse code from other projects have higher success. We also demonstrate knowledge spillovers from projects connected via common programmers.

If interested, contact Tomi Männistö for further information

Hybrid software development (TOPIC AREA)

The current pandemic has brought many, even radical, changes to almost all software companies and software development organizations. Especially the sudden moves to working from home (WFH) in March 2020 forced them to adapt and even rethink many software engineering practices in order to continue productive software development under the new constraints.

Now (December 2021), various hybrid ways of working appear to become the new "normal" for the software industry in general. For instance, many companies are offering flexible workplace arrangements (WFX).

This thesis theme aims to explore and possibly explain such changes in contemporary software engineering. Potential research questions include the following:

  • How has the COVID-19 pandemic affected different software engineering activities (negatively or positively)? What are the mechanisms?
  • What adaptations and countermeasures have different software organizations devised to cope with the challenges?
  • What could be learned from them for future hybrid software development processes, practices and tools?

Contact: Petri Kettunen

MLOps -- as a derivative of DevOps -- is about practice and tools for ML-based systems that technically enable iterative software engineering practice. We have several funded positions in the area of MLOps in our research projects (IMLE4 and AIGA ) that can be tailored to the interest of the applicant. For details, contact Mikko Raatikainen ( [email protected] ).

Digital Twin of Yourself

Digital twins are virtual world dynamic models of real-world physical objects. They originate from manufacturing domains. In such environments, they are utilized, for example, for predictive maintenance of equipment based on real-time machine data.

Recently the application domains of digital twins have broadened to cover living objects – especially human beings, for instance, in medical domains (so-called Human Digital Twins). In this thesis topic, the objective is to design a digital twin of yourself. The choice of the digital twin dynamic model is free, and so are the data inputs. One possibility could be, for instance, your real-life physical exercise data (e.g., from a heart-rate monitor). You could also consider your Citizen Digital Twin, following your study data and yourself as a lifelong learner.

Software engineering and climate change (TOPIC AREA)

Global climate change may have various impacts on future software engineering on the one hand, and software engineering may affect climate change directly or indirectly, positively or negatively on the other hand. All that opens up many potentially important research problems. Specific theses in this topic area could be, for instance, the following themes:

  • Green IT (e.g., engineering new software with energy-efficiency requirements in order to reduce or limit power consumption and consequently the carbon footprint)
  • Carbon neutrality goals of software companies (e.g., software development organizations decreasing physical travelling in order to reduce their greenhouse gas emissions)
  • Developing software products or services for measuring climate change-related factors

The thesis could be a literature review, an empirical case study or a scientific design work.

Life-long learning for the modern software engineering profession

Specific intended learning outcomes for computer science (software engineering) graduates are life-long learning skills. Such skills and capabilities are essential in modern industrial software engineering environments. Workplace learning is a vital part of most professional software development jobs. What are the necessary life-long learning skills exactly? Why are those skills and capabilities essential in different software organizations? How can they be learned and improved? How do software professionals learn in their workplaces? What particular skills will be more critical in the future? Why? This topic could be investigated by case studies in real-life software organizations. The specific research questions could be some of the above or possibly focused on particular skills (e.g., assessing one's own and the works of other software developers). Contact: Petri Kettunen

Software development in non-ICT contexts (TOPIC AREA)

Software technology is increasingly applied in non-ICT domains and environments (e.g., healthcare, financial sector, telecommunications systems, industrial automation). Such conditions bring up many considerations for effective and efficient software engineering, such as: What are the key characteristics of different use domains (e.g., complexity, reliability)? What is the scope of the particular software system? How are the software requirements engineered? What are the specific constraints (e.g., regulations) in different domains to be considered in software engineering? How to measure the success of software projects and products? What software development methods (e.g., agile) are applicable in different domains? Why/why not? What particular software-related competencies are needed (e.g., digitalization, IoT, cyber-physical systems)? This research problem could be investigated theoretically (literature study) and empirically in industrial case studies. The actual research questions could be some of the above or formulated individually. Contact: Petri Kettunen

Creatively self-adaptive software architectures (TOPIC AREA)

We have recently started exciting research in the intersection between the research fields of self-adaptive software and computational creativity, intending to develop novel software architectures that can creatively adapt themselves in unforeseen situations. This initiative is a new research collaboration between the Discovery Group of Prof. Hannu Toivonen and ESE. There are different options for thesis work with either of the groups. To get a better idea of the topic, see Linkola et al. 2017. Aspects of Self-awareness: An Anatomy of Metacreative Systems.… Contact: Tomi Männistö

Continuous Experimentation (TOPIC AREA)

Software product and service companies need capabilities to evaluate their development decisions and customer and user value. Continuous experimentation, as an experiment-driven development approach, may reduce such development risks by iteratively testing product and service assumptions critical to the software's success. Experiment-driven development has been a crucial component of software development, especially in the last decade. Companies such as Microsoft, Facebook, Google, Amazon and many others often conduct experiments to base their development decisions on data collected from field usage.  Contact: Tomi Männistö

Digitalization and digital transformations: impacts on software engineering and systems development (TOPIC AREA)

Digitalization is nowadays cross-cutting and inherent in most areas of businesses and organizations. Software is increasingly built-in and ubiquitous. Such trends and developments bring up many potential software research problems, such as: What does digitalization entail in different contexts? How should digitalization be taken into account in software development processes? What is the role of customer/user involvement in software-intensive systems development (e.g., digital services)? What are the key quality attributes? What new software engineering skills and competencies may be needed? What is the role of software (and IT) in general in different digital transformations (e.g., vs business process development)? How is digitalization related to traditional software engineering and computer science disciplines in different contexts? What aspects of software development and digital technologies are fundamentally new or different from the past? This research problem could be investigated theoretically (literature study) or empirically in industrial case studies. The actual research questions could be some of the above or formulated individually. Contact: Petri Kettunen

High-performing software teams (TOPIC AREA)

How is (high) performance defined and measured in software development (e.g., productivity)? Which factors affect it - positively or negatively - and how strongly (e.g., development tools, team composition)? Can we "build" high-performing software teams systematically, or do they merely emerge under certain favourable conditions? What are suitable organizational designs and environments for hosting and supporting such teams? See this link and this link for more info. Contact: Petri Kettunen

Software innovation (TOPIC AREA)

How are innovation and creativity taken into account in software development processes and methods (e.g., Agile)? What role do customer/user input and feedback play in software(-intensive) product creation (e.g., open innovation)? How to define and measure 'innovativeness' in software development? What makes software development organizations (more) innovative? See here for more about the topic. How can Open Data Software help innovation? Contact: Petri Kettunen

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Digital Commons @ USF > College of Engineering > Computer Science and Engineering > Theses and Dissertations

Computer Science and Engineering Theses and Dissertations

Theses/dissertations from 2023 2023.

Refining the Machine Learning Pipeline for US-based Public Transit Systems , Jennifer Adorno

Insect Classification and Explainability from Image Data via Deep Learning Techniques , Tanvir Hossain Bhuiyan

Brain-Inspired Spatio-Temporal Learning with Application to Robotics , Thiago André Ferreira Medeiros

Evaluating Methods for Improving DNN Robustness Against Adversarial Attacks , Laureano Griffin

Analyzing Multi-Robot Leader-Follower Formations in Obstacle-Laden Environments , Zachary J. Hinnen

Secure Lightweight Cryptographic Hardware Constructions for Deeply Embedded Systems , Jasmin Kaur

A Psychometric Analysis of Natural Language Inference Using Transformer Language Models , Antonio Laverghetta Jr.

Graph Analysis on Social Networks , Shen Lu

Deep Learning-based Automatic Stereology for High- and Low-magnification Images , Hunter Morera

Deciphering Trends and Tactics: Data-driven Techniques for Forecasting Information Spread and Detecting Coordinated Campaigns in Social Media , Kin Wai Ng Lugo

Deciphering Trends and Tactics: Data-driven Techniques for Forecasting Information Spread and Detecting Coordinated Campaigns in Social Media , Kin Wai NG Lugo

Automated Approaches to Enable Innovative Civic Applications from Citizen Generated Imagery , Hye Seon Yi

Theses/Dissertations from 2022 2022

Towards High Performing and Reliable Deep Convolutional Neural Network Models for Typically Limited Medical Imaging Datasets , Kaoutar Ben Ahmed

Task Progress Assessment and Monitoring Using Self-Supervised Learning , Sainath Reddy Bobbala

Towards More Task-Generalized and Explainable AI Through Psychometrics , Alec Braynen

A Multiple Input Multiple Output Framework for the Automatic Optical Fractionator-based Cell Counting in Z-Stacks Using Deep Learning , Palak Dave

On the Reliability of Wearable Sensors for Assessing Movement Disorder-Related Gait Quality and Imbalance: A Case Study of Multiple Sclerosis , Steven Díaz Hernández

Securing Critical Cyber Infrastructures and Functionalities via Machine Learning Empowered Strategies , Tao Hou

Social Media Time Series Forecasting and User-Level Activity Prediction with Gradient Boosting, Deep Learning, and Data Augmentation , Fred Mubang

A Study of Deep Learning Silhouette Extractors for Gait Recognition , Sneha Oladhri

Analyzing Decision-making in Robot Soccer for Attacking Behaviors , Justin Rodney

Generative Spatio-Temporal and Multimodal Analysis of Neonatal Pain , Md Sirajus Salekin

Secure Hardware Constructions for Fault Detection of Lattice-based Post-quantum Cryptosystems , Ausmita Sarker

Adaptive Multi-scale Place Cell Representations and Replay for Spatial Navigation and Learning in Autonomous Robots , Pablo Scleidorovich

Predicting the Number of Objects in a Robotic Grasp , Utkarsh Tamrakar

Humanoid Robot Motion Control for Ramps and Stairs , Tommy Truong

Preventing Variadic Function Attacks Through Argument Width Counting , Brennan Ward

Theses/Dissertations from 2021 2021

Knowledge Extraction and Inference Based on Visual Understanding of Cooking Contents , Ahmad Babaeian Babaeian Jelodar

Efficient Post-Quantum and Compact Cryptographic Constructions for the Internet of Things , Rouzbeh Behnia

Efficient Hardware Constructions for Error Detection of Post-Quantum Cryptographic Schemes , Alvaro Cintas Canto

Using Hyper-Dimensional Spanning Trees to Improve Structure Preservation During Dimensionality Reduction , Curtis Thomas Davis

Design, Deployment, and Validation of Computer Vision Techniques for Societal Scale Applications , Arup Kanti Dey

AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing , Hamza Elhamdadi

Automatic Detection of Vehicles in Satellite Images for Economic Monitoring , Cole Hill

Analysis of Contextual Emotions Using Multimodal Data , Saurabh Hinduja

Data-driven Studies on Social Networks: Privacy and Simulation , Yasanka Sameera Horawalavithana

Automated Identification of Stages in Gonotrophic Cycle of Mosquitoes Using Computer Vision Techniques , Sherzod Kariev

Exploring the Use of Neural Transformers for Psycholinguistics , Antonio Laverghetta Jr.

Secure VLSI Hardware Design Against Intellectual Property (IP) Theft and Cryptographic Vulnerabilities , Matthew Dean Lewandowski

Turkic Interlingua: A Case Study of Machine Translation in Low-resource Languages , Jamshidbek Mirzakhalov

Automated Wound Segmentation and Dimension Measurement Using RGB-D Image , Chih-Yun Pai

Constructing Frameworks for Task-Optimized Visualizations , Ghulam Jilani Abdul Rahim Quadri

Trilateration-Based Localization in Known Environments with Object Detection , Valeria M. Salas Pacheco

Recognizing Patterns from Vital Signs Using Spectrograms , Sidharth Srivatsav Sribhashyam

Recognizing Emotion in the Wild Using Multimodal Data , Shivam Srivastava

A Modular Framework for Multi-Rotor Unmanned Aerial Vehicles for Military Operations , Dante Tezza

Human-centered Cybersecurity Research — Anthropological Findings from Two Longitudinal Studies , Anwesh Tuladhar

Learning State-Dependent Sensor Measurement Models To Improve Robot Localization Accuracy , Troi André Williams

Human-centric Cybersecurity Research: From Trapping the Bad Guys to Helping the Good Ones , Armin Ziaie Tabari

Theses/Dissertations from 2020 2020

Classifying Emotions with EEG and Peripheral Physiological Data Using 1D Convolutional Long Short-Term Memory Neural Network , Rupal Agarwal

Keyless Anti-Jamming Communication via Randomized DSSS , Ahmad Alagil

Active Deep Learning Method to Automate Unbiased Stereology Cell Counting , Saeed Alahmari

Composition of Atomic-Obligation Security Policies , Yan Cao Albright

Action Recognition Using the Motion Taxonomy , Maxat Alibayev

Sentiment Analysis in Peer Review , Zachariah J. Beasley

Spatial Heterogeneity Utilization in CT Images for Lung Nodule Classication , Dmitrii Cherezov

Feature Selection Via Random Subsets Of Uncorrelated Features , Long Kim Dang

Unifying Security Policy Enforcement: Theory and Practice , Shamaria Engram

PsiDB: A Framework for Batched Query Processing and Optimization , Mehrad Eslami

Composition of Atomic-Obligation Security Policies , Danielle Ferguson

Algorithms To Profile Driver Behavior From Zero-permission Embedded Sensors , Bharti Goel

The Efficiency and Accuracy of YOLO for Neonate Face Detection in the Clinical Setting , Jacqueline Hausmann

Beyond the Hype: Challenges of Neural Networks as Applied to Social Networks , Anthony Hernandez

Privacy-Preserving and Functional Information Systems , Thang Hoang

Managing Off-Grid Power Use for Solar Fueled Residences with Smart Appliances, Prices-to-Devices and IoT , Donnelle L. January

Novel Bit-Sliced In-Memory Computing Based VLSI Architecture for Fast Sobel Edge Detection in IoT Edge Devices , Rajeev Joshi

Edge Computing for Deep Learning-Based Distributed Real-time Object Detection on IoT Constrained Platforms at Low Frame Rate , Lakshmikavya Kalyanam

Establishing Topological Data Analysis: A Comparison of Visualization Techniques , Tanmay J. Kotha

Machine Learning for the Internet of Things: Applications, Implementation, and Security , Vishalini Laguduva Ramnath

System Support of Concurrent Database Query Processing on a GPU , Hao Li

Deep Learning Predictive Modeling with Data Challenges (Small, Big, or Imbalanced) , Renhao Liu

Countermeasures Against Various Network Attacks Using Machine Learning Methods , Yi Li

Towards Safe Power Oversubscription and Energy Efficiency of Data Centers , Sulav Malla

Design of Support Measures for Counting Frequent Patterns in Graphs , Jinghan Meng

Automating the Classification of Mosquito Specimens Using Image Processing Techniques , Mona Minakshi

Models of Secure Software Enforcement and Development , Hernan M. Palombo

Functional Object-Oriented Network: A Knowledge Representation for Service Robotics , David Andrés Paulius Ramos

Lung Nodule Malignancy Prediction from Computed Tomography Images Using Deep Learning , Rahul Paul

Algorithms and Framework for Computing 2-body Statistics on Graphics Processing Units , Napath Pitaksirianan

Efficient Viewshed Computation Algorithms On GPUs and CPUs , Faisal F. Qarah

Relational Joins on GPUs for In-Memory Database Query Processing , Ran Rui

Micro-architectural Countermeasures for Control Flow and Misspeculation Based Software Attacks , Love Kumar Sah

Efficient Forward-Secure and Compact Signatures for the Internet of Things (IoT) , Efe Ulas Akay Seyitoglu

Detecting Symptoms of Chronic Obstructive Pulmonary Disease and Congestive Heart Failure via Cough and Wheezing Sounds Using Smart-Phones and Machine Learning , Anthony Windmon

Toward Culturally Relevant Emotion Detection Using Physiological Signals , Khadija Zanna

Theses/Dissertations from 2019 2019

Beyond Labels and Captions: Contextualizing Grounded Semantics for Explainable Visual Interpretation , Sathyanarayanan Narasimhan Aakur

Empirical Analysis of a Cybersecurity Scoring System , Jaleel Ahmed

Phenomena of Social Dynamics in Online Games , Essa Alhazmi

A Machine Learning Approach to Predicting Community Engagement on Social Media During Disasters , Adel Alshehri

Interactive Fitness Domains in Competitive Coevolutionary Algorithm , ATM Golam Bari

Measuring Influence Across Social Media Platforms: Empirical Analysis Using Symbolic Transfer Entropy , Abhishek Bhattacharjee

A Communication-Centric Framework for Post-Silicon System-on-chip Integration Debug , Yuting Cao

Authentication and SQL-Injection Prevention Techniques in Web Applications , Cagri Cetin

Multimodal Emotion Recognition Using 3D Facial Landmarks, Action Units, and Physiological Data , Diego Fabiano

Robotic Motion Generation by Using Spatial-Temporal Patterns from Human Demonstrations , Yongqiang Huang

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Dissertations / Theses on the topic 'Software engineering'

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Le, Gal Thierry. "Re-engineering software for integration using computer aided software engineering." Thesis, This resource online, 1992.


Bondesson, Tobias. "Software Engineering Education Improvement : An Assessment of a Software Engineering Programme." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2004.

Riehle, Richard D. "An engineering context for software engineering." Monterey, Calif. : Naval Postgraduate School, 2008.

Lim, Edwin C. "Software metrics for monitoring software engineering projects." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 1994.

Sezer, Bulent. "Software Engineering Process Improvement." Master's thesis, METU, 2007.

Boriani, Dario V. "Software engineering for control." Thesis, University of Oxford, 1989.

Arciniegas-Mendez, Maryi. "Regulation in Software Engineering." Thesis, Proceedings of the Eighth International Workshop on Cooperative and Human Aspects of Software Engineering, 2015.

Loomes, Martin James. "Software engineering curriculum design." Thesis, University of Surrey, 1991.

Alrabghi, Leenah O. "QFD IN SOFTWARE ENGINEERING." Kent State University / OhioLINK, 2014.

Zamli, Kamal Zuhairi. "Supporting software processes for distributed software engineering teams." Thesis, University of Newcastle Upon Tyne, 2003.

Karvonen, T. (Teemu). "Continuous software engineering in the development of software-intensive products:towards a reference model for continuous software engineering." Doctoral thesis, Oulun yliopisto, 2017.

Pawar, Sourabh A. "A Common Software Development Framework For Coordinating Usability Engineering and Software Engineering Activities." Thesis, Virginia Tech, 2004.

Hanssen, Geir Kjetil. "From Agile Software Product Line Engineering Towards Software Ecosystems." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2010.


Ahmad, M. O. (Muhammad Ovais). "Exploring Kanban in software engineering." Doctoral thesis, Oulun yliopisto, 2016.

Chennamsetty, Harish. "Experimentation in Global Software Engineering." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2015.

Masoud, F. A. "Quality metrics in software engineering." Thesis, University of Liverpool, 1987.

Geyer-Schulz, Andreas, and Michael Hahsler. "Software engineering with analysis patterns." Institut für Informationsverarbeitung und Informationswirtschaft, WU Vienna University of Economics and Business, 2001.

Wang, Yingxu. "Software engineering process modelling analysis." Thesis, Southampton Solent University, 1998.

Cunningham, Hamish. "Software architecture for language engineering." Thesis, University of Sheffield, 2000.

Gabriel, Pedro Hugo do Nascimento. "Software languages engineering: experimental evaluation." Master's thesis, Faculdade de Ciências e Tecnologia, 2010.

Bull, Christopher Neil. "Studios in software engineering education." Thesis, Lancaster University, 2016.

Watson, Cody. "Deep Learning In Software Engineering." W&M ScholarWorks, 2020.

Burge, Janet E. "Software Engineering Using design RATionale." Link to electronic thesis, 2005.

Rönkkö, Kari. "Software Practice from the Inside : Ethnography Applied to Software Engineering." Licentiate thesis, Karlskrona : Blekinge Institute of Technology, 2002.

Meridji, Kenza. "Analysis of software engineering principles from an engineering perspective." Mémoire, École de technologie supérieure, 2010.

Brophy, Dennis J. O'Leary James D. "Software evaluation for developing software reliability engineering and metrics models /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1999.

Brophy, Dennis J., and James D. O'Leary. "Software evaluation for developing software reliability engineering and metrics models." Thesis, Monterey, California ; Naval Postgraduate School, 1999.

Lin, Chia-en. "Performance Engineering of Software Web Services and Distributed Software Systems." Thesis, University of North Texas, 2014.

Delorey, Daniel Pierce. "Observational Studies of Software Engineering Using Data from Software Repositories." Diss., CLICK HERE for online access, 2007.

McMeekin, David Andrew. "A software inspection methodology for cognitive improvement in software engineering." Thesis, Curtin University, 2010.

Kinnula, A. (Atte). "Software process engineering in a multi-site environment:an architectural design of a software process engineering system." Doctoral thesis, University of Oulu, 1999.

Jennings, Charles A. "Re-engineering software systems in the Department of Defense using integrated computer aided software engineering tools." Thesis, Monterey, California. Naval Postgraduate School, 1992.

Addy, Edward A. "Verification and validation in software product line engineering." Morgantown, W. Va. : [West Virginia University Libraries], 1999.

Freund, Tessen. "Software Engineering durch Modellierung wissensintensiver Entwicklungsprozesse /." Berlin : GITO, 2007.

Schroeder, Andreas. "Software engineering perspectives on physiological computing." Diss., lmu, 2011.

Nojoumian, Mehrdad. "Document engineering of complex software specifications." Thesis, University of Ottawa (Canada), 2007.

Freund, Tessen. "Software Engineering durch Modellierung wissensintensiver Entwicklungsprozesse." Berlin GITO, 2006.

Rodden, Thomas. "Supporting cooperation in software engineering environments." Thesis, Lancaster University, 1990.

Mannering, D. P. "Problem Oriented Engineering for Software Safety." Thesis, Open University, 2010.

Cook, Carl Leslie Raymond. "Towards Computer-Supported Collaborative Software Engineering." Thesis, University of Canterbury. Computer Science and Software Engineering, 2007.

Heineman, Judie A. "A software reliability engineering case study." Thesis, Monterey, California. Naval Postgraduate School, 1996.

Unterkalmsteiner, Michael. "Coordinating requirements engineering and software testing." Doctoral thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2015.

Karatasios, Labros G. "Software engineering with database management systems." Thesis, Monterey, California. Naval Postgraduate School, 1989.

Greer, Desmond. "Software engineering risk : understanding and management." Thesis, University of Ulster, 2000.

Martin, W. J. "App Store Analysis for software engineering." Thesis, University College London (University of London), 2017.

Yang, Bob 1976. "Managing a distributed software engineering team." Thesis, Massachusetts Institute of Technology, 1998.

Rantanen, E. (Eetu). "Requirements engineering in agile software projects." Bachelor's thesis, University of Oulu, 2017.

Zabardast, Ehsan. "Towards Understanding Assets in Software Engineering." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2021.

California State University, San Bernardino


Computer Science and Engineering Theses, Projects, and Dissertations

Theses/projects/dissertations from 2024 2024.


Recommendation System using machine learning for fertilizer prediction , Durga Rajesh Bommireddy

Classification of Remote Sensing Image Data Using Rsscn-7 Dataset , Satya Priya Challa

Cultural Awareness Application , Bharat Gupta


AUTOMATED BRAIN TUMOR CLASSIFIER WITH DEEP LEARNING , venkata sai krishna chaitanya kandula


Crash Detecting System Using Deep Learning , Yogesh Reddy Muddam


Theses/Projects/Dissertations from 2023 2023






Heart Disease Prediction Using Binary Classification , Virendra Sunil Devare



Sales and Stock Management System , Rashmika Gaddam Ms









TWITTER POLICING , Hemanth Kumar Medisetty





Brain Tumor Detection Using MRI Images , Mayur Patel



Pillow Based Sleep Tracking Device Using Raspberry Pi , Venkatachalam Seviappan








Machine Learning for Kalman Filter Tuning Prediction in GPS/INS Trajectory Estimation , Peter Wright

Theses/Projects/Dissertations from 2022 2022







Improved Sensor-Based Human Activity Recognition Via Hybrid Convolutional and Recurrent Neural Networks , Sonia Perez-Gamboa

College of Education FileMaker Extraction and End-User Database Development , Andrew Tran


Theses/Projects/Dissertations from 2021 2021

A General Conversational Chatbot , Vipin Nambiar

Verification System , Paras Nigam


Ahmedabad City App , Rushabh Picha


ANDROID PARKING SYSTEM , Vishesh Reddy Sripati

Sentiment Analysis: Stock Index Prediction with Multi-task Learning and Word Polarity Over Time , Yue Zhou

Theses/Projects/Dissertations from 2020 2020




Theses/Projects/Dissertations from 2019 2019





Theses/Projects/Dissertations from 2018 2018


California State University, San Bernardino Chatbot , Krutarth Desai

ORGANIZE EVENTS MOBILE APPLICATION , Thakshak Mani Chandra Reddy Gudimetla








Theses/Projects/Dissertations from 2017 2017






Custom T-Shirt Designs , Ranjan Khadka



PIPPIN MACHINE , Kiran Reddy Pamulaparthy


I2MAPREDUCE: DATA MINING FOR BIG DATA , Vishnu Vardhan Reddy Sherikar




Theses/Projects/Dissertations from 2016 2016


CoyoteLab - Linux Containers for Educational Use , Michael D. Korcha



Theses/Projects/Dissertations from 2015 2015


Density Based Data Clustering , Rayan Albarakati

Developing Java Programs on Android Mobile Phones Using Speech Recognition , Santhrushna Gande




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  • Publications
  • Student Projects

Completed student projects

Supporting multiple proof engines by translating between intermediate verification languages [details] Master's thesis, March 2015 — September 2015 Author: Michael Ameri Supervisor: Carlo A. Furia

Implementation of a hint system for solving Java programming exercises [details] Bachelor's thesis, January 2015 — August 2015 Author: Baumann Cedric Supervisor: urica Nikoli and Marco Piccioni

First steps towards a web framework for an automated Eiffel code teaching assistant [details] Bachelor's thesis, February 2015 — July 2015 Author: Christian Vonrüti Supervisor: Marco Piccioni

The impact of requirements in distributed software development: an empirical study [details] Master's Thesis, December 1st, 2014 - June 1st, 2015 Author: Marc Egg Supervisor: Martin Nordio and Christian Estler

Real-time Conflict Awareness for Distributed Version Control Systems [details] Master's Thesis, November 2014 - April 2015 Author: Fabian Gremper Supervisor: Martin Nordio and Christian Estler

Modelling and Verifying an Object-Oriented Concurrency Model in GROOVE [details] Master's Thesis, October 2014 — April 2015 Author: Claudio Corrodi Supervisor: Chris Poskitt and Alexander Heußner (University of Bamberg)

Eiffel Inspector Improvements [details] Bachelor's Thesis, September 2014 — March 2015 Author: Samuel Schmid Supervisor: Julian Tschannen

Robot control by user tracking with a laser range scanner [details] Master's Thesis, August 2014 — February 2015 Author: Ivo Steinmann Supervisor: Jiwon Shin

AutoTeach: incremental hints for programming exercises [details] Master's thesis, March 2014 — September 2014 Author: Paolo Antonucci Supervisor: Marco Piccioni

Parallelism visualizer for SCOOP [details] Master's Thesis, July 2014 — December 2014 Author: Dominic Meier Supervisor: Mischael Schill

Mantra: Eiffel as a web service [details] Internship, May 2014 — July 2014 Author: Manav Kedia Supervisor: Martin Nordio and Christian Estler

Graphical user interface for Roboscoop applications [details] Bachelor's Thesis, April 2014 — October 2014 Author: Jonas Stulz Supervisor: Andrey Rusakov

Gesture-based user interface [details] Master's Thesis, April 2014 — October 2014 Author: David Itten Supervisor: Jiwon Shin, Andrey Rusakov

Concurrency patterns in SCOOP [details] Master's Thesis, March 2014 — September 2014 Author: Roman Schmocker Supervisor: Alexey Kolesnichenko

Distributed testing sessions for AutoTest [details] Master's Thesis at the University of Lorraine (France), March 2014 — September 2014 Author: Victorien Elvinger Supervisor: Chris Poskitt, Alexey Kolesnichenko, and Max (Yu) Pei

A constraint-based layout manager for Eiffel [details] Master's thesis, November 2013 — May 2014 Author: Emanuele Rudel Supervisor: Đurica Nikolić

Rule-based code analysis [details] Master's thesis, October 2013 — April 2014 Author: Stefan Zurfluh Supervisor: Julian Tschannen

Application of SCOOP to Mission Control in Robotics [details] Research in Computer Science project, September 2013 — February 2014 Author: Ganesh Ramanathan Supervisors: Benjamin Morandi, Sebastian Nanz, Stéphane Magnenat

Implementing and evaluating an exception mechanism for SCOOP [details] Master's thesis, March 2013 — September 2013 Author: Florian Besser Supervisor: Benjamin Morandi

Loop invariant inference from postconditions in EVE [details] Bachelor's thesis, November 2012 — June 2013 Author: Michael Ameri Supervisors: Carlo A. Furia and Julian Tschannen

Model-based contracts for Java / C# collections [details] Bachelor's thesis and EiffelStudio Lab, May 2012 — June 2013 Author: Tobias Kiefer Supervisor: Nadia Polikarpova

Spell checker [report] Software Engineering Laboratory, September 2012 — January 2013 Author: Benjamin Fischer Supervisor: Julian Tschannen

Syntax Highlighting for Eiffel on the web Software Engineering Laboratory, September 2012 — January 2013 Author: Trisha Kothari Supervisor: Julian Tschannen

Diff library in Eiffel (Diffeif) [details] Bachelor's Thesis, September 2012 — January 2013 Author: Rafael Wampfler Supervisor: Max Pei

Extending CloudStudio with a collaborative remote debugger [details] Master's Thesis at Politecnico di Milano, January 2012 — December 2012 Author: Rand Nezha and Mert Tufekci Supervisor: Elisabetta Di Nitto, Martin Nordio and Christian Estler

Refinements and Git Integration with Notifications and Monitoring [details] Software Engineering Laboratory: Open Source Eiffel Studio — November 2012 Author: Christopher Dentel Supervisor: Martin Nordio and Christian Estler

News and Notification: Propagating Relevant Changes to Developers [details] Software Engineering Laboratory: Open Source Eiffel Studio — February 2012 Author: Christopher Dentel Supervisor: Martin Nordio and Christian Estler

Monitors: Keeping Informed on Code Changes [details] Independent Research Study — November 2012 Author: Christopher Dentel Supervisor: Martin Nordio and Christian Estler

Automatic Version Control System for Distributed Software Development [details] Master's Thesis, March 2012 — September 2012 Author: Sandra Weber Supervisor: Martin Nordio and Christian Estler

Awareness in CloudStudio [details] Internship, May 2012 — July 2012 Author: Brian Bullins Supervisor: Martin Nordio and Christian Estler

A Comparative Study of Programming Models for Concurrency [details] Bachelor's Thesis at UFRGS (Brazil), November 2011 — Juli 2012 Author: Kaue Soares da Silveira Supervisor: Sebastian Nanz

An executable structural operational semantics for SCOOP [details] Master's Thesis, October 2011 — April 2012 Author: Mischael Schill Supervisor: Benjamin Morandi

A Mac OS X EiffelVision port based on a generated Cocoa wrapper [details] Bachelor's Thesis, October 2011 — February 2012 Author: Emanuele Rudel Supervisor: Benjamin Morandi

Purity Checker [details] --> Software Engineering Laboratory, September — December 2011 Authors: Antoine Kaufmann, Reto Wyss Supervisors: Nadia Polikarpova, Scott West

Successful outsourcing: Necessary conditions and best practices [details] Master's Thesis at MTEC (ETH), October 2011 — December 2011 Author: Johannes Schneider Supervisor: Martin Nordio and Christian Estler

A web-based IDE for Java [details] Software Engineering Laboratory, September — December 2011 Author: Marcel Bertsch Supervisor: Martin Nordio and Christian Estler

Revision control support for a web-based IDE [details] Software Engineering Laboratory, September — December 2011 Author: Roland Meyer Supervisor: Martin Nordio and Christian Estler

Fine-grained aspects of automatic refactoring in C2Eiffel [details] Master Thesis, April 2011 — September 2011 Author: Adrian Friedli Supervisor: Marco Trudel

Implementing an IRC Server Using an Object-Oriented Programming Model for Concurrency [details] Bachelor Thesis, April 2011 — July 2011 Author: Fabian Gremper Supervisor: Scott West

Version control in Eve [details] Software Engineering Laboratory, March 2011 — August 2011 Author: Emanuele Rudel Supervisor: Nadia Polikarpova

Eiffel HTTP Server [details] Bachelor's Thesis, February 2011 — May 2011 Author: Florian Besser Supervisor: Scott West

Developing JavaScript applications in Eiffel [details] Master's Thesis, December 2010 — May 2011 Author: Alexandru Dima Supervisor: Martin Nordio and Christian Estler

Model-based contracts for C# collections [details] Master's Thesis, Tver State University (Russia), February 2011 — April 2011 Author: Elena Mokhon Supervisor: Nadia Polikarpova

Objective-C Frameworks to Eiffel Converter [details] Master's Thesis, May 2009 — November 2010 Author: Matteo Cortonesi Supervisor: Benjamin Morandi

Capture and Replay Framework for Eiffel [details] Master's Thesis, April 2010 — September 2010 Author: Arno Fiva Supervisor: Yi Wei

Applying Data Mining to Contract Inference [details] Master's Thesis, March 2010 — August 2010 Author: Nikolay Kazmin Supervisor: Yi Wei

Reproducible executions of SCOOP programs [details] Research Project, June 2010 — September 2010 Authors: Andrey Nikonov and Andrey Rusakov Supervisors : Sebastian Nanz, Benjamin Morandi, Scott West

Proof transforming compilation for Separation logic [details] Master's Thesis at Wuhan University - China, December 2009 — June 2010 Author: Tang Mei Supervisor: Martin Nordio

Integrating an Automatic Version Control System into EiffelStudio [details] Engineer Thesis at Hanoi University of Technology, December 2009 — April 2010 Author: Do Le Minh Supervisor: Martin Nordio

An integrated development environment (IDE) for Distributed Software Engineering [details] Engineer Thesis at Hanoi University of Technology, December 2009 — April 2010 Author: Le Minh Duc Supervisor: Martin Nordio

Mutation Tool for Eiffel Code Transformation [details] Master's Thesis, December 2009 — May 2010 Author: Stefan Buchholz Supervisor: Yi Wei

Automated Object-Oriented Software Testing using Genetic Algorithms and Static Analysis [details] Master's Thesis, September 2009 — March 2010 Author: Lucas S. Silva Supervisor: Yi Wei

Profiling SCOOP Programs [details] Master's Thesis, November 2009 — April 2010 Author: Martino Trosi Supervisor: Benjamin Morandi

Implementation of Advanced SCOOP Aspects [details] Master's Thesis, October 2009 — April 2010 Author: Damien Müllhaupt Supervisor: Benjamin Morandi

Application of SCOOP in Robotic Control [details] Research Project, September 2009 — December 2009 Author: Ganesh Ramanathan Supervisors : Sebastian Nanz, Benjamin Morandi, Scott West

SCOOP in Practice [details] Research in Computer Science II, June 2009 — December 2009 Author: Mohammad Seyed Alavi Supervisor: Sebastian Nanz

Eclipse Eiffel Development Toolkit - EDT [details] Master's Thesis, June 2009 — November 2009 Author: Reto Ohnsorg Supervisor: Marco Trudel

TrucStudio [details] Master's Thesis, April 2009 — October 2009 Author: Gerry Kammerer Supervisor: Michela Pedroni

Object State Exploration [details] Master's Thesis, March 2009 — September 2009 Author: Serge Gebhardt Supervisor: Yi Wei

Relation between Quality of an OO System and Multiple Inheritance — An Exploration Master's Thesis, October 2008 — April 2009 Author: David Stokar Supervisor: Yi Wei

EiffelVision for Mac OS X [details] Master Thesis, March 2009 — September 2009 Author: Daniel Furrer Supervisor: Benjamin Morandi

Integrating SCOOP into EVE [details] Master Thesis, March 2009 — September 2009 Author: Patrick Huber Supervisor: Benjamin Morandi

Improving relevancy of dynamically-inferred contracts in Eiffel [details] Diploma Thesis, February 2009 — June 2009 Author: Flaviu Roman Supervisor: Nadia Polikarpova

A system to support the faculty hiring process [details] Master Thesis, November 2008 — May 2009 Author: Matthias Loeu Supervisor: Marco Piccioni

Multi-Format, EiffelStudio-integrated Object Browser and Writer [details] Master Thesis, November 2008 — May 2009 Author: Lucien Hansen Supervisor: Marco Piccioni

Automatic Verification of Eiffel Agents [details] Master Thesis, October 2008 — April 2009 Author: Julian Tschannen Supervisor: Martin Nordio

Embedding Proof-Carrying Components into Isabelle [details] Master Thesis, September 2008 — March 2009 Author: Bruno Hauser Supervisor: Martin Nordio

Metrics Calculation for Object-oriented language Software Engineering Lab, Summer 2008 Author: Tobias Heinzen Supervisor: Yi Wei

From Research Prototype to Field Test: Lessons Learned Master thesis Author: Stefan Mori Supervisor: Andreas Leitner

Capture and Replay for Eiffel Master Thesis Author: Stefan Sieber Supervisor: Andreas Leitner

TrucStudio - Bug fixing and graph refactoring [details] Semester thesis, Summer 2008 Author: Damien Mullhaupt Supervisor: Michela Pedroni

Dynamic assertion inference in a programming language with Design by Contract support (Eiffel case study) Master Thesis, May 2007 — June 2008 Author: Nadia Polikarpova Supervisor: Ilinca Ciupa

TrucStudio - Automatic modeling of courses [details] Master thesis, 18 February 2008 — 17 August 2008 Author: Adrian Muller Supervisor: Michela Pedroni

TrucStudio - Refactoring clusters [details] Master thesis, 18 February 2008 — 17 August 2008 Author: Florian Geldmacher Supervisor: Michela Pedroni

Comparing Courses in TrucStudio [details] Software Engineering Lab, Summer 2008 Author: Peter von Rohr Supervisor: Michela Pedroni

Integrating Proof-Transforming Compilation into EiffelStudio [details] Master Thesis, February 2008 — August 2008 Author: Manuel Hess Supervisor: Martin Nordio

Proof-Transforming Compilation of Eiffel Contracts [details] Diploma Thesis, January 2008 — May 2008 Author: Hasan Karahan Supervisor: Martin Nordio

DEFCON - Development of a Db4o-Eiffel Connector [details] Master thesis, October 2007 — April 2008 Author: Ruihua Jin Supervisor: Marco Piccioni

TrucStudio - Course Management [details] Master thesis, August 2007 — February 2008 Author: Lukas Angerer Supervisor: Michela Pedroni

TrucStudio - Output Generation [details] Master thesis, August 2007 — February 2008 Author: Enrico Albonico Supervisor: Michela Pedroni

OWL Importer for TrucStudio [details] Semester thesis, Fall 2007 Author: Pascal Goffin Supervisor: Michela Pedroni

Examples for Touch of Class [details] Software Engineering Lab, Fall 2007 Author: Corinne Muller and Damien Mullhaupt Supervisor: Michela Pedroni

ESCHER: Eiffel Schema Evolution Support [details] Research in Computer Science, Fall 2007 Author: Matthias Loeu Supervisor: Marco Piccioni

JXTA implementation for Eiffel [details] Master thesis, September 2006 — March 2007 Author: Beat Strasser

Single Sign-On for Origo [details] Master thesis, September 2006 — March 2007 Author: Samuele Lucchini

Origo Core [details] Master thesis, SS 2007 Author: Patrick Ruckstuhl Supervisor: Till Bay

TrucStudio - A course management tool [details] Master thesis, March 2007 — September 2007 Author: Michele Croci Supervisor: Michela Pedroni

Complete Contracts for EiffelBase [details] Semester Thesis, SS2007 Author: Marco Zietzling Supervisor: Bernd Schoeller

Guided Random-Based Testing Strategies [details] Diploma thesis, February 2007 — June 2007 Author: Cosmin Mitran Supervisor: Ilinca Ciupa

TrucStudio - A prototype [details] Master thesis, October 2006 — April 2007 Author: Leo Widmer Supervisor: Michela Pedroni

Traffic 3.2 - Improving Random Building Placement [details] Semester thesis, WS 2006/2007 Author: Florian Hotz Supervisor: Michela Pedroni

Traffic 3.2 - Finding Suitable Examples to Assist Students' Learning [details] Semester thesis, WS 2006/2007 Author: Franziska Fritschi Supervisor: Michela Pedroni

Implementing a Proof-Transforming Compiler from Eiffel to CIL [details] Semester thesis, July 2006 — February 2007 Author: Michel Guex Supervisor: Martin Nordio

Traffic 3.1 - Examples for Eiffel beginners [details] Semester thesis, July 2006 — February 2007 Author: Roger Imbach Supervisor: Michela Pedroni

Traffic 3.1 - Getting started [details] Semester thesis, July 2006 — February 2007 Author: Matthias Loeu Supervisor: Michela Pedroni

Vision2 Cocoa backend [details] Semester thesis, July 2006 — December 2006 Author: Jann Roder, Ueli Peter, Roland Hausler

EiffelMedia [details] Semester thesis, SS 2006 Author: Kaspar Rohrer, Urs Doenni, Matthias Buhlmann, Philipp Krahenbuhl, Dominik Kaser

Traffic 3.1 - Getting Started [details] Semester thesis, July2006 — December 2006 Author: Matthias Loeu Supervisor: Michela Pedroni

SMIL Editor for EiffelMedia [details] Semester thesis, SS 2006 Author: David Huber and Stefan Mori

Field study and clasiffication of faults in Eiffel [details] Diploma thesis, SS 2006 Author: Raluca Borca-Muresan

Visualizing graphs with Vision2 [details] Semester thesis, SS 2006 Author: Lukas Angerer

Traffic 3.1 - Enhancing Visualization and Performance of Traffic [details] Master thesis, SS 2006 Author: Alan Fehr

Traffic 3.1 - Introducing roads [details] Semester thesis, SS 2006 Author: Michele Croci

Transations in SCOOP [details] Master thesis, SS 2006 Author: Daniel Moser

Traffic 3.1 - Designing Suitable Examples [details] Semester thesis, SS 2006 Author: Sarah Hauser

Traffic 3.0 - Extracting Software Examples for Pedagogical Effectiveness [details] Semester thesis, WS 2005/2006 Author: Susanne Kasper

Designing a User Interface for the Innovative E-mail Client Framework [details] Semester thesis, WS 2005/2006 Author: Alexandra Burns

AutoTest - Automated fault localization in external C code of Eiffel programs [details] Semester thesis, WS 2005/2006 Author: Reto Ghioldi

EiffelMedia [details] Semester thesis, WS 2005/2006 Author: Rafael Bischof, Peter Wyss

Traffic 3.0 - Introducing time into a city model [details] Semester thesis, WS 2005/2006 Author: Florian Geldmacher

Traffic 3.0 ? Realistic buildings and performance [details] Semester thesis, WS 2005/2006 Author: Fabian Wuest

Designing an Innovative E-mail Client [details] Master thesis, SS 2005 Author: Andrea Rezzonico

Wrapping a complex C++ library for Eiffel [details] Semester thesis, SS 2005 Author: Simon Reinhard

Resolving Name-Clashes in Eiffel [details] Semester thesis, SS 2005 Author: Alan Fehr

Object-Oriented Numerical Interpolation Component in Eiffel [details] Bachelor thesis, SS 2005 Author: Benjamin W|thrich

Proving the Deutsch-Schorr-Waite Algorithm using Path Properties [details] Semester thesis, SS 2005 Author: Ronny Zakhejm

Survey of Persistence Approaches [details] Master thesis, SS 2005 Author: Shinji Takasaka

Round-trip Engineering of .NET assemblies [details] Semester thesis, SS 2005 Author: Matthias Konrad

EiffelMedia [details] Semester thesis, SS 2005 Authors: Martin Seiler, Marco Stoeckli, Robert Weiser, Ueli Weiss, Lukas Naef, Yves Alter, Urs Doenni, Jonas Rutishauser, Julian Tschannen, Marco Senn, Pascal Rota

City 3D - A frontend for Traffic [details] Semester thesis, SS 2005 Author: Stefan Daniel & Valentin Wustholz

FLAT_HUNT redesign and ESDL extensions [details] Semester thesis, SS 2005 Author: Ursina Caluori

Touch redesign [details] Semester thesis, SS 2005 Author: Roger Kung

Design and implementation of a run-time mechanism for deadlock detection in SCOOP [details] Semester project, SS 2005 Author: Daniel Moser

Steps to Automatic Component Certification [details] Master thesis, SS 2005 Author: Sibylle Aregger

Eiffel to Java Compiler [details] Diploma thesis, SS 2005 Author: Benno Baumgartner

Contract Prover [details] Semester thesis, WS 2004/2005 Author: Daniel Kistler

ESDL [details] Semester thesis, WS 2004/2005 Author: Patrick Ruckstuhl

Component Assessment Server [details] Master thesis, WS 2004/2005 Author: Samuele Milani

Object-Oriented Framework for Teaching Introductory Programming [details] Master thesis, WS 2004/2005 Author: Rolf Bruderer

Redesign of the TRAFFIC library [details] Semesterarbeit, WS 2004/2005 Author: Sibylle Aregger

Exercise Design for Introductory Programming - "Learn-by-doing" basic OO-concepts using Inverted Curriculum [details] Master thesis, SS 2004, March 2004 — September 2004 Author: Marcel Kessler

ESDL - Sound API Extensions and Antialiasing [details] Semesterarbeit, SS 2004 Author: Yann Muller

Code Crawler [details] Semesterarbeit, SS 2004 Author: Andri Toggenburger

Component Server [details] Semesterarbeit, SS 2004 Author: Samuele Milani

Extending the Eiffel library for data structures and algorithms: EiffelBase [details] Master thesis, SS 2004 Author: Olivier Jeger

Precondition Enforcement Analysis for Quality Assurance [details] Master thesis, SS 2004 Author: Nadja Beeli

Contract Wizard II: Developing a GUI [details] Diplomarbeit, SS 2004 Author: Petra Marty

Reflection Library for Eiffel [details] Master thesis, SS 2004 Author: Beat Fluri

Further development of the Test Wizard (An automatic test tool based on Design by Contract) [details] Student project, SS 2004, March 2004 — July 2004 Author: Ilinca Ciupa (guest at Chair of Software Engineering)

Formal Semantic Specification of a Core Object-Oriented Language [details] Diplomarbeit SS 2004 Author: Thomas Bietenhader

Reusable Mathematical Models [details] Master thesis, January 2004 — July 2004 Author: Tobias Widmer

Exception Handling in SCOOP [details] Diplomarbeit WS 2003/2004, January 2004 — March 2004 Author: Christopher Nenning

Reimplementation of Elevator control application using EiffelVision [details] Semesterarbeit WS 2003/2004 Author: Erwin Betschart

Distance Vector Routing using SCOOP [details] Semesterarbeit WS 2003/2004 Author: Emmanuel Python

ESDL - Eiffel Simple Direct Media Library [details] Semesterarbeit WS 2003/2004 Author: Benno Baumgartner

Test Wizard: Automatic test generation based on Design by Contract [details] Master project WS 2003/2004, July 2003 — January 2004 Author: Nicole Greber

Contract Wizard II [details] Diplomarbeit SS 2003, June 2003 — October 2003 Author: Dominik Wotruba

Eiffel SDL multimedia library (ESDL) [details] Diplomarbeit SS 2003, May 2003 — September 2003 Author: Till Bay

Eiffel library to generate Java bytecodes [details] Diplomarbeit SS 2003, May 2003 — September 2003 Author: Daniel Gisel

Automatic Contract Extraction: Developing a CIL Parser [details] Diplomarbeit SS 2003, May 2003 — September 2003 Author: Christoph Marti

Teaching introductory programming with the Inverted Curriculum approach [details] Diplomarbeit SS 2003, May 2003 — September 2003 Author: Michela Pedroni

Catching CATs - Towards a fully typesafe Eiffel [details] Diplomarbeit SS 2003, March 2003 — July 2003 Author: Markus Keller

Comparison of .NET and Java threading [details] Semesterarbiet WS 2002/2003 Author: Axel Wathne

Turning design patterns into reusable components [details] Semesterarbeit WS 2002/2003 Author: Anders Haugeto

GUI for student management [details] Semesterarbeit WS 2002/2003 Author: Dominik Wotruba

Exploration of the Suitability of O-O Techniques for the Design and Implementation of a Numeric Math Library using Eiffel [details] Diplomarbeit, October 2002 — February 2003 Author: Peter Hafliger

EiffelUnits [details] Semesterarbeit SS 2002 Author: Markus Keller

Eiffel conformant Wrapper Classes for the .NET Threading Library [details] Semesterarbeit SS 2002 Author: Judith Zimmermann

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Technical University of Munich

  • Chair of Software and Systems Engineering
  • TUM School of Computation, Information and Technology
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Technical University of Munich

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The Future of AI Research: 20 Thesis Ideas for Undergraduate Students in Machine Learning and Deep Learning for 2023!

A comprehensive guide for crafting an original and innovative thesis in the field of ai..

By Aarafat Islam on 2023-01-11

“The beauty of machine learning is that it can be applied to any problem you want to solve, as long as you can provide the computer with enough examples.” — Andrew Ng

This article provides a list of 20 potential thesis ideas for an undergraduate program in machine learning and deep learning in 2023. Each thesis idea includes an  introduction , which presents a brief overview of the topic and the  research objectives . The ideas provided are related to different areas of machine learning and deep learning, such as computer vision, natural language processing, robotics, finance, drug discovery, and more. The article also includes explanations, examples, and conclusions for each thesis idea, which can help guide the research and provide a clear understanding of the potential contributions and outcomes of the proposed research. The article also emphasized the importance of originality and the need for proper citation in order to avoid plagiarism.

1. Investigating the use of Generative Adversarial Networks (GANs) in medical imaging:  A deep learning approach to improve the accuracy of medical diagnoses.

Introduction:  Medical imaging is an important tool in the diagnosis and treatment of various medical conditions. However, accurately interpreting medical images can be challenging, especially for less experienced doctors. This thesis aims to explore the use of GANs in medical imaging, in order to improve the accuracy of medical diagnoses.

2. Exploring the use of deep learning in natural language generation (NLG): An analysis of the current state-of-the-art and future potential.

Introduction:  Natural language generation is an important field in natural language processing (NLP) that deals with creating human-like text automatically. Deep learning has shown promising results in NLP tasks such as machine translation, sentiment analysis, and question-answering. This thesis aims to explore the use of deep learning in NLG and analyze the current state-of-the-art models, as well as potential future developments.

3. Development and evaluation of deep reinforcement learning (RL) for robotic navigation and control.

Introduction:  Robotic navigation and control are challenging tasks, which require a high degree of intelligence and adaptability. Deep RL has shown promising results in various robotics tasks, such as robotic arm control, autonomous navigation, and manipulation. This thesis aims to develop and evaluate a deep RL-based approach for robotic navigation and control and evaluate its performance in various environments and tasks.

4. Investigating the use of deep learning for drug discovery and development.

Introduction:  Drug discovery and development is a time-consuming and expensive process, which often involves high failure rates. Deep learning has been used to improve various tasks in bioinformatics and biotechnology, such as protein structure prediction and gene expression analysis. This thesis aims to investigate the use of deep learning for drug discovery and development and examine its potential to improve the efficiency and accuracy of the drug development process.

5. Comparison of deep learning and traditional machine learning methods for anomaly detection in time series data.

Introduction:  Anomaly detection in time series data is a challenging task, which is important in various fields such as finance, healthcare, and manufacturing. Deep learning methods have been used to improve anomaly detection in time series data, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for anomaly detection in time series data and examine their respective strengths and weaknesses.

bachelor thesis topics software engineering

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6. Use of deep transfer learning in speech recognition and synthesis.

Introduction:  Speech recognition and synthesis are areas of natural language processing that focus on converting spoken language to text and vice versa. Transfer learning has been widely used in deep learning-based speech recognition and synthesis systems to improve their performance by reusing the features learned from other tasks. This thesis aims to investigate the use of transfer learning in speech recognition and synthesis and how it improves the performance of the system in comparison to traditional methods.

7. The use of deep learning for financial prediction.

Introduction:  Financial prediction is a challenging task that requires a high degree of intelligence and adaptability, especially in the field of stock market prediction. Deep learning has shown promising results in various financial prediction tasks, such as stock price prediction and credit risk analysis. This thesis aims to investigate the use of deep learning for financial prediction and examine its potential to improve the accuracy of financial forecasting.

8. Investigating the use of deep learning for computer vision in agriculture.

Introduction:  Computer vision has the potential to revolutionize the field of agriculture by improving crop monitoring, precision farming, and yield prediction. Deep learning has been used to improve various computer vision tasks, such as object detection, semantic segmentation, and image classification. This thesis aims to investigate the use of deep learning for computer vision in agriculture and examine its potential to improve the efficiency and accuracy of crop monitoring and precision farming.

9. Development and evaluation of deep learning models for generative design in engineering and architecture.

Introduction:  Generative design is a powerful tool in engineering and architecture that can help optimize designs and reduce human error. Deep learning has been used to improve various generative design tasks, such as design optimization and form generation. This thesis aims to develop and evaluate deep learning models for generative design in engineering and architecture and examine their potential to improve the efficiency and accuracy of the design process.

10. Investigating the use of deep learning for natural language understanding.

Introduction:  Natural language understanding is a complex task of natural language processing that involves extracting meaning from text. Deep learning has been used to improve various NLP tasks, such as machine translation, sentiment analysis, and question-answering. This thesis aims to investigate the use of deep learning for natural language understanding and examine its potential to improve the efficiency and accuracy of natural language understanding systems.

bachelor thesis topics software engineering

Photo by  UX Indonesia  on  Unsplash

11. Comparing deep learning and traditional machine learning methods for image compression.

Introduction:  Image compression is an important task in image processing and computer vision. It enables faster data transmission and storage of image files. Deep learning methods have been used to improve image compression, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for image compression and examine their respective strengths and weaknesses.

12. Using deep learning for sentiment analysis in social media.

Introduction:  Sentiment analysis in social media is an important task that can help businesses and organizations understand their customers’ opinions and feedback. Deep learning has been used to improve sentiment analysis in social media, by training models on large datasets of social media text. This thesis aims to use deep learning for sentiment analysis in social media, and evaluate its performance against traditional machine learning methods.

13. Investigating the use of deep learning for image generation.

Introduction:  Image generation is a task in computer vision that involves creating new images from scratch or modifying existing images. Deep learning has been used to improve various image generation tasks, such as super-resolution, style transfer, and face generation. This thesis aims to investigate the use of deep learning for image generation and examine its potential to improve the quality and diversity of generated images.

14. Development and evaluation of deep learning models for anomaly detection in cybersecurity.

Introduction:  Anomaly detection in cybersecurity is an important task that can help detect and prevent cyber-attacks. Deep learning has been used to improve various anomaly detection tasks, such as intrusion detection and malware detection. This thesis aims to develop and evaluate deep learning models for anomaly detection in cybersecurity and examine their potential to improve the efficiency and accuracy of cybersecurity systems.

15. Investigating the use of deep learning for natural language summarization.

Introduction:  Natural language summarization is an important task in natural language processing that involves creating a condensed version of a text that preserves its main meaning. Deep learning has been used to improve various natural language summarization tasks, such as document summarization and headline generation. This thesis aims to investigate the use of deep learning for natural language summarization and examine its potential to improve the efficiency and accuracy of natural language summarization systems.

bachelor thesis topics software engineering

Photo by  Windows  on  Unsplash

16. Development and evaluation of deep learning models for facial expression recognition.

Introduction:  Facial expression recognition is an important task in computer vision and has many practical applications, such as human-computer interaction, emotion recognition, and psychological studies. Deep learning has been used to improve facial expression recognition, by training models on large datasets of images. This thesis aims to develop and evaluate deep learning models for facial expression recognition and examine their performance against traditional machine learning methods.

17. Investigating the use of deep learning for generative models in music and audio.

Introduction:  Music and audio synthesis is an important task in audio processing, which has many practical applications, such as music generation and speech synthesis. Deep learning has been used to improve generative models for music and audio, by training models on large datasets of audio data. This thesis aims to investigate the use of deep learning for generative models in music and audio and examine its potential to improve the quality and diversity of generated audio.

18. Study the comparison of deep learning models with traditional algorithms for anomaly detection in network traffic.

Introduction:  Anomaly detection in network traffic is an important task that can help detect and prevent cyber-attacks. Deep learning models have been used for this task, and traditional methods such as clustering and rule-based systems are widely used as well. This thesis aims to compare deep learning models with traditional algorithms for anomaly detection in network traffic and analyze the trade-offs between the models in terms of accuracy and scalability.

19. Investigating the use of deep learning for improving recommender systems.

Introduction:  Recommender systems are widely used in many applications such as online shopping, music streaming, and movie streaming. Deep learning has been used to improve the performance of recommender systems, by training models on large datasets of user-item interactions. This thesis aims to investigate the use of deep learning for improving recommender systems and compare its performance with traditional content-based and collaborative filtering approaches.

20. Development and evaluation of deep learning models for multi-modal data analysis.

Introduction:  Multi-modal data analysis is the task of analyzing and understanding data from multiple sources such as text, images, and audio. Deep learning has been used to improve multi-modal data analysis, by training models on large datasets of multi-modal data. This thesis aims to develop and evaluate deep learning models for multi-modal data analysis and analyze their potential to improve performance in comparison to single-modal models.

I hope that this article has provided you with a useful guide for your thesis research in machine learning and deep learning. Remember to conduct a thorough literature review and to include proper citations in your work, as well as to be original in your research to avoid plagiarism. I wish you all the best of luck with your thesis and your research endeavors!

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Humboldt-Universität zu Berlin - Faculty of Mathematics and Natural Sciences - Software Engineering

Bachelor and master thesis.

Currently, the Software Engineering group offers bachelor's and master's theses on the following subjects.

Workflow for bachelor / master theses written at the chair

Bachelor Thesis

AST Based Fault Localization ( pdf )

  • Grey-box Fuzzing with well-formed Models (keine Ausschreibung, bitte Email mit dem Titel: “[Thesis]-Grey-Box Fuzzing” to [email protected]....)
  • Optimising Coupling and Cohesion Metrics for Modular Neural Networks(keine Ausschreibung,  bitte Email mit dem Titel: “[Thesis]-ModularNN” to [email protected]....)
  • From Scripts to Computational Materials Science Data Analysis Workflows ( pdf )
  • **NEU** Fuzzing for Model Transformation Engines ( pdf )
  • **NEU** Evaluating Fuzzing for Model-Driven Software Engineering (MDSE) Tools ( pdf )

Bachelor/ Master  Thesis*

  • Diagnosis and Localization of Memory Leaks ( pdf )
  • Analyzing Parameter Tuning in Search-Based Test Case Generation Techniques ( pdf )
  • STARDUST II - Systematic Architecture Level Fault Diagnosis Using Statistical Techniques ( pdf )
  • Grammar-Based Fuzzing for LibreOffice ( pdf )
  • Grammar-Based Repair for Open Office Documents ( pdf )
  • Grammar-Based Generation of Debugging Hypotheses for Libre Office ( pdf )
  • .. Requirement Prioritization and Release Planning Problems ( pdf )
  • .. Automatic Program Repair Problems ( pdf )
  • .. Regression Test Suite Generation and Augmentation Problems ( pdf )
  • .. Test Case Selection and Prioritization Problems ( pdf )
  • Automated Validation of Patch Correctness and Maintainability with Symbolic Execution ( pdf )
  • Automated Documentation of Source Code ( pdf )
  • Vulnerability Detection with Character Level Language Models for Python (keine Ausschreibung, bitte Email mit dem Titel: “[Thesis]-Vulnerability Detection” to [email protected]....)
  • Automatic Repair of Software Vulnerabilities (keine Ausschreibung, bitte Email mit dem Titel: “[Thesis]-Vulnerability Repair” to [email protected]....)  
  • Metamorphic Testing in Computational Materials Science Data Analysis Workflows ( pdf )
  • Voice-driven specification with Python SpeechRecognition (keine Ausschreibung,  bitte Email mit dem Titel: “[Thesis]-Voice-driven-specification” to [email protected]....)
  • **NEU** Voice-driven Test-Case Generation (keine Ausschreibung,  bitte Email mit dem Titel: “[Thesis]-Voice-driven-specification” to [email protected]....)
  • **NEU** Optimizing a Neural Network Approach for the Reverse Transformation of Spin-Wave-Theory ( pdf )

*The problem can be adapted to the requirements of a Bachelor or Master Thesis.

Master  Thesis

  • Adaptive Genetic Algorithms in Search-Based Software Engineering ( pdf )
  • An Evaluation of Metaheuristic Search Strategies for Automatic Software Repair ( pdf )
  • Fault Localization and Debugging with Probabilistic Slicing ( pdf )
  • Code Generation from Natural Language Documentation ( pdf )
  • **NEU** Generating Semantically Correct Programs with seq2tree Transformer Networks (keine Ausschreibung, bitte Email mit dem Titel: “[Thesis]-seq2tree Program Generation” to [email protected]....)
  • **NEU**  Automatic Reverse Engineering Scientific Models from Scientific Software (keine Ausschreibung, bitte Email mit dem Titel: “[Thesis]-Scientific Models” to [email protected]....)
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M.Tech/Ph.D Thesis Help in Chandigarh | Thesis Guidance in Chandigarh

bachelor thesis topics software engineering

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bachelor thesis topics software engineering


bachelor thesis topics software engineering


Software Engineering is a branch that deals with the development and evolution of software products by employing certain methodologies and well-defined scientific principles. For developing a software product certain processes need to be followed and outcome of which is an efficient and authentic software product. The software is a group of executable program code with associated libraries. Software designed to satisfy a specific need is known as Software Product. It is a very good topic for master’s thesis, project, and research. There are various topics in Software Engineering which will be helpful for M.Tech and other masters students write their software project thesis.

Latest thesis topics in software engineering for research scholars:

  • Fault detection in software using biological techniques
  • Enhancement in MOOD metrics for software maintainability and reliability
  • To enhance effort estimation using Function point analysis in Cocomo model
  • To evaluate and improve model based mutation technique to detect test cases error in product line testing
  • To propose improvement in genetic algorithm to calculate function dependency in test case prioritization in regression testing
  • To propose dynamic technique with static metrics to check coupling between software modules
  • To propose improvement TYPE 4 clone detection in clone testing

Find the link at the end to download the latest thesis and research topics in software engineering

Software Evolution

Software Evolution is the process of developing software product using underlying techniques and methodologies. It consists of all the steps right from the initial requirements up to its maintenance. In the initial stage, software requirements are gathered. After this, a prototype of the actual software product is created which is shown to the end users for feedback. Users give their suggestions regarding the product and suggest changes if required. This process is repeated until the time desired software product is developed.  There are certain Software Evolution laws according to which software is divided into following three types:

  • S-Type (static-type) – This type of software works according to specifications and solutions. It is the simplest of all the three types of software.
  • P-Type (practical-type) – This software is a collection of procedures. Gaming software is an example of this type of software.
  • E-Type (embedded-type) – This software works according to the real-world requirements. It has a high degree of evolution.

The methods and steps taken to design a software product are referred to as software paradigms .

Why is Software Engineering required?

Software Engineering is required due to frequent changes in user requirements and the environment. Through your thesis and research work, you can get to know more about the importance of Software Engineering. Following are the other things for which software engineering is required:

  • Large Software – The large size of software make it essential for the requirement of software engineering.
  • Scalability – Software Engineering makes it possible to scale the existing software rather than creating a new software.
  • Cost – Software Engineering also cut down the excess manufacturing cost in software development.
  • Dynamic Nature of Software – Software Engineering plays an important role if new enhancements are to be done in the existing software provided that the nature of software is dynamic.
  • Better Quality Management – Software Engineering provides better software development processes for better quality services.

Software Development Lifecycle (SDLC)

SDLC is a sequence of steps and stages in Software Engineering for the development of Software product. It is an important topic for project and thesis in software engineering. Following are the phases of SDLC:

Thesis in software engineering

  • Requirement Gathering and Analysis – It is the initial stage of software development in which the requirements for the software product to be made is collected. In this phase, the engineering team studies existing systems, take the opinion of stakeholders, and conduct user interviews. The types of requirements include user requirements, functional requirements and non-functional requirements. After the requirements are collected, these are examined and analyzed for validation i.e. whether these requirements can be incorporated into the system or not.
  • Feasibility Study – After requirement gathering, the next step is the feasibility study i.e. to check whether the desired software system can be made or not. The software development team comes up with an outline of the whole process and discusses whether the system will be able to meet the user requirements or not. In this phase, all the aspects like financial, practical, and technical are considered. If these aspects are found to be feasible only then the further processes are taken up.
  • Software Design – After confirming the feasibility of the software system, the designing of the software product is done. The designing of the software is done based on the requirements collected in the initial stage. An outline of the whole process is created in this phase which will define the overall system architecture. There are two types of designs – physical design and logical design.
  • Coding – This phase is also known as implementation phase as the actual implementation of the software system takes place here. An executable programming code is written in any suitable programming language for implementation. The work is divided into different modules and coding is done in each of these modules. This process is undertaken by a developer expert in programming.
  • Testing – Testing phase follows the coding phase in which testing of the code is done to check whether the system meets the user requirements or not. The types of testing include unit testing, system testing, integration testing and acceptance testing. Testing is required to find out any underlying errors and bugs in the product. Testing helps in creating a reliable software product.
  • Deployment – After successful testing, the software product is delivered to the end users. Customers perform Beta Testing to find out if there are changes required in the system or not. If changes are needed, then they can suggest them to the engineering team.
  • Maintenance – A special team is appointed to look after the maintenance of the software product. This team will provide timely software updates and give notifications based on that. The code is updated in accordance with the changes taking place in the real world environment.

Software Development Process Models

There are certain software development models as defined by Software Paradigms. Some of these are explained below:

Waterfall Model

It is a simple model for software development which defines that all the phases of SDLC take place in a linear manner. Simple meaning that if one phase is finished then only the next phase is started. According to this model, all the phases are executed in sequence with the planning of next phase in the previous phase. Also, this model will not function properly if there are certain issues left in the previous phase.

bachelor thesis topics software engineering

Iterative Model

It is another model for software development in which the whole process takes place in iterations. Iteration simply means repeating steps after a cycle is over. On the first iteration, the software is developed on a small scale and then the subsequent steps are followed.  During the next iteration, more features and modules are added. On completion of each iteration cycle, software is produced which have their own features and capabilities. The management team works on the risk management and prepare for next iteration.

bachelor thesis topics software engineering

Spiral Model

Spiral Model is a combination of iterative model and any one of the other SDLC model. The most important feature of this model is the consideration of risk factor which left unnoticed by other models. Initially, the objectives and constraints of the software product are determined. During next iteration, the prototype of the software is created. This process also includes risk analysis. In the fourth phase, next iteration is prepared.

bachelor thesis topics software engineering

In the waterfall model, we can go to next step only if the previous step is completed. Also, we cannot go back to the previous stage if some change is required. This drawback of waterfall model is fulfilled by the V-Shaped Model which provides testing of each phase in a reverse manner. In this model, test plans and test cases are created according to the requirements of that stage to verify and validate the software product. Thus verification and validation go in parallel in this case.

bachelor thesis topics software engineering

Software Metrics and Measures

Software Metrics and Measures are essential components in Software Engineering to understand the attributes and aspects of a software. These also help in maintaining the better quality of the software products. Following are some of the Software Metrics:

  • Size Metrics – It is measured in terms of Lines of Code (LOC) and Function Point Code. Lines of Code mean the number of lines of the programming code whereas Function Point Code is the Functional capacity of the software.
  • Complexity Metrics – It is measured in terms of number of independent paths in a program.
  • Quality Metrics – It is determined by the number of defects encountered while developing the software and after the product is delivered.
  • Process Metrics – Methods, tools, and standards used in software development come under process metrics.
  • Resource Metrics – It includes effort, time and resources used in development process.

Modularization in Software Engineering

Modularization is a technique in Software Engineering in which software system is divided into multiple modules and each module carries out its individual task independently. Modularization is more or less based on ‘Divide and Conquer’ approach. Each module is compiled and executed separately.

Advantages of Modularization are:

  • Smaller modules are easier to process.
  • Modularization offers a level of abstraction to the program.
  • High Cohesion components can be used again.
  • Concurrent execution is also possible.
  • It is also more secure.

Software Testing

It is the process of verifying and validating the software product to check whether it meets the user requirements or not as expected. Moreover, it also detects underlying defects, errors, and bugs that left unnoticed during the process of software development. As a whole, software testing detects software failures. Software Testing itself is a sub-field in software engineering and a trending topic for project, thesis, and research in software engineering.

Purpose of Software Testing

Following are the main purposes of software testing:

  • Verification – Verification is a process to find out whether the developed software product meets the business requirements or not. Verification ensures that whether the product being created satisfies the design specifications or not.
  • Validation – Validation is the process that examines whether or not the system meets the user requirements. The validation process is carried out at the end of SDLC.
  • Defect Finding – Defect finding simply means the difference between the actual output and the expected output. Software Testing tends to find this defect in the software product.

Types of Testing

Following are the main types of testing in software systems:

  • Alpha Testing – It is the most common type of testing carried out by a developer team at the developer end. It is conducted before the product is released.
  • Beta Testing – It is a type of software testing carried out by end users at the user end. This type of testing is performed in a real-world environment.
  • Acceptance Testing – It is a type of testing to find out whether the software system meets the user requirements or not.
  • Unit Testing – It is a type of testing in which an individual unit of the software product is tested.
  • Integration Testing – In this, two or more modules are combined and tested together as a group.
  • System Testing – Here all the individual modules are combined and then tested as a single group.

UML and Software Engineering

UML or Unified Modeling Language is language in software engineering for visualizing and documenting the components of a software system and is created by Object Management Group (OMG). It is different from programming languages. UML implements object-oriented concepts for analysis and design.

Building Blocks of UML

Following are the three main building blocks of UML:


Things can be any one of the following:

Structural – Static Components of a system

Behavioral – Dynamic Components of a system

Grouping – Group elements of a UML model like package

Annotational – Comments of a UML model

The relationship describes how individual elements are associated with each other in a system. Following kinds of relationships are there:

  • Association
  • Generalization
  • Realization

The output of the entire process is UML diagrams. Following are the main UML diagrams:

  • Class Diagram
  • Object Diagram
  • Use Case Diagram
  • Sequence Diagram
  • Collaboration Diagram
  • Activity Diagram
  • Statechart Diagram
  • Deployment Diagram
  • Component Diagram

Software Maintenance

After the Software product is successfully launched in the market, timely updations and modifications needed to be done. This all comes under Software Maintenance. It includes all those measures taken after the delivery to correct errors and to enhance the performance. Software Maintenance does not merely means fixing defects but also providing time to time updations.

Types of Software Maintenance

The types of Software Maintenance depends upon the size and nature of the software product. Following are the main types of software maintenance:

  • Corrective Maintenance –  Fixing and correcting a problem identified by the user comes under corrective maintenance.
  • Adaptive Maintenance –  In adaptive maintenance, the software is kept up-to-date to meet the ever-changing environment and technology.
  • Perfective Maintenance –  To keep the software durable, perfective maintenance is done. This includes the addition of new features and new user requirements.
  • Preventive Maintenance –  To prevent any future problems in the software, preventive maintenance is done so that there are not any serious issues in near future.

Activities in Software Maintenance

Following activities are performed in Software Maintenance as given by IEEE:

  • Identification and Tracing
  • Implementation
  • System Testing
  • Acceptance Testing
  • Maintenance Management

Reverse Engineering

Reverse Engineering is a process in which an existing system is thoroughly analyzed to extract some information from that system and reproduce that system or product using that extracted information.  The whole process is a reverse SDLC. Reverse Engineering for software is done to extract the source code of the program which can be implemented in a new software product.

Case Tools for Software Engineering

Case or Computer-aided Software Engineering are computer-based automated tools for development and maintenance of software products. Just as the CAD (Computer-aided design) is used for designing of hardware products, Case is used for designing of software products. Case tools develop high-quality and easily maintainable software products.

Elements of Case Tools

Following are the main components of Case Tools:

  • Central Repository –  Central Repository or Data Dictionary is a central storage for product specifications, documents, reports, and diagrams.
  • Upper Case Tools – These are used in planning, analysis, and design phases of SDLC.
  • Lower Case Tools – These are used in the implementation, testing, and maintenance.
  • Integrated Case Tools – These tools can be used in all the stages of SDLC.

Project, Thesis, and Research topics in Software Engineering

Following is the list of Software Engineering topics for project, thesis, and research for masters and other postgraduate students:

  • Data Modeling

Software Models

Software Quality

Verification and Validation

Software Project Management

Data Modeling 

The process of structuring and organizing data is known as Data Modeling. After structuring of data, it is implemented in the database system. While organizing data, certain constraints and limitations are also applied to data. The main function of Data Modeling is to manage a large amount of both structured and unstructured data. In data modeling, initially, a conceptual data model is created which is later translated to the physical data model.

UML(Unified Modeling Language)

This was all about Software Engineering. You can explore and research more of this topic while working on your project and thesis. It is a standard language to visualize software systems. This language is used by software developers, business analysts, software architects, and other individuals to study the artifacts of a software system. It is a very good topic for a thesis in Software Engineering.

SDLC or Software Development Lifecycle is a set of stages followed for the development of a software product. For building a software product steps are followed beginning from data collection to software maintenance. It also includes software testing in which a software goes through various types of testing before giving a final nod to the software product.

Masters students can work on software models for their thesis work. Various types of software models are there like waterfall model, V-Shaped model, spiral model, prototype model, agile model, Iterative model etc. These models give step by step implementation of various phases of software development.

The concept of ontology is used in Software Engineering to represent the domain knowledge in a formal way. Certain knowledge-based applications use the ontology to share knowledge. Ontology is used in software engineering to collaborate the use of AI techniques in software engineering. UML diagrams are also being used in the development of Ontology.

Software Quality refers to the study of software features both external and internal taking into consideration certain attributes. External features mean how software is performing in a real-world environment while internal features refer to the quality of code written for the software. External quality is dependent on the internal in the sense that software works in the real-world environment with respect to the code written by the coder.

After the software product is implemented, it goes through the testing phase to find any underlying error or bug. The most common type of software testing is the alpha testing. In this type of testing, the software is tested to detect any issue before it is released. Students can find a number of topics under software testing for thesis, research, and project.

Software Maintenance is necessary as some errors or bugs can be detected in future in the software product. Students can study and research on the types of software maintenance done by the team. Software Maintenace does not solely means fixing errors in the software. It includes a number of tasks done so that the software product keeps on working perfectly with advancements.

Verification and Validation are the two most important steps in software engineering. Verification and Validation are not as easy as it seems. There are a number of steps under it which can be an interesting research work for your thesis. Verification is done before validation.

It is another interesting topic for the thesis in software engineering. It refers to the management of the software project through proper planning and execution. It includes time, cost, quality, and scope of the project. A team is appointed for this purpose.

These were the topics in software engineering for project, thesis, and research. Contact us for any kind of thesis help in software engineering for M.Tech and Ph.D.

Click the following link to download Latest Thesis and Research Topics in Software Engineering

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Kennesaw State master’s graduate makes the personal academic

KENNESAW, Ga. | Jun 4, 2024

William Reed

So, he broke new ground.

Reed, who earned his master’s degree in exercise science , connected his love of soccer with his academic interest in human performance, earning acclaim along the way. In 2023, he earned honors at the Symposium of Student Scholars and at Wellstar College of Health and Human Service ’s Research and Engagement Day. In April, he won the Graduate College ’s Three-Minute Thesis competition, all for his research into proper conditioning and analyses of running form for soccer officials.

“Soccer teams and leagues invest millions in their players, but I haven’t seen that kind of investment in officials—conditioning them or studying their work,” said Reed, who has officiated soccer games for three years. “My courses and research at KSU led me to deep study on this topic, and I hope to keep looking into it.”

In soccer, an official, known as the assistant referee, runs along the sideline carrying a flag, which adversely affects running form. Reed sought empirical data on those effects while working as a graduate assistant with associate professor of exercise science Garrett Hester.

“Most of the subjects’ movements and associated variables—whether that be forced production, whether it be acceleration variables, whether it be velocity or power—are all reduced significantly when they're holding that flag,” said Reed, whose thesis was titled “Kinetic and Kinematic Effects of Unilateral Flag Carrying on Referee Sprinting and Agility Performance.” “Whatever the mechanism is, when they're holding that flag, performance does in fact decrease.”

Senior lecturer of exercise science Kevin Huet, who officiates college and professional soccer games, first started working with Reed during Reed’s undergraduate studies; Reed also earned his bachelor’s in exercise science from KSU in 2022. Huet advised the undergraduate exercise science student group and stayed in touch with Reed as he advanced in his studies. When Reed got the idea for his thesis, he sought Huet’s advice.

“I’ve done similar research on soccer referees, but William came up with this idea on his own,” Huet said. “When he presented it to me, I was practically jumping out of my chair. I was so excited to see a student advancing this research, and I’ve been honored to help him out these past couple of years.”

Huet had contacts in various leagues, and he provided them to Reed for the research. Reed, in turn, worked with officials at multiple levels, from youth through college, and even included one subject with certification from FIFA, the world’s governing body for soccer. From the perspective of an official, Reed said he was happy to expand the base of knowledge on soccer officials beyond studies simply scrutinizing the accuracy of in-game officiating decisions.

Down the road, Reed hopes to publish his thesis in exercise science and strength and conditioning journals, touting the novelty of the research into an understudied realm. He wouldn’t rule out a doctoral degree focusing on sprinting mechanics or conditioning for soccer officials.

“If there's something you're interested in, chances are there is at least one professor who will support you if not multiple professors,” he said. “There is space for a person to explore what they want to. The world, and academia, only grow because of students taking, doing research and exploring their interests. Since KSU fosters that environment, I can't really think of a better compliment for an academic institution.”

– Story by Dave Shelles

Photos by Darnell Wilburn Jr.

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A leader in innovative teaching and learning, Kennesaw State University offers undergraduate, graduate and doctoral degrees to its more than 45,000 students. Kennesaw State is a member of the University System of Georgia with 11 academic colleges. The university’s vibrant campus culture, diverse population, strong global ties and entrepreneurial spirit draw students from throughout the country and the world. Kennesaw State is a Carnegie-designated doctoral research institution (R2), placing it among an elite group of only 7 percent of U.S. colleges and universities with an R1 or R2 status. For more information, visit .

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For current information about the demonstrations, see External link . 

bachelor thesis topics software engineering

Presentation Master’s Thesis - Moritz Hausmann - Clinical Psychology

Roeterseilandcampus - Gebouw G, Straat: Nieuwe Achtergracht 129-B, Ruimte: GS.09

Introduction: University life can induce a lot of stress, which leads to mental health problems for some students. Many interventions that are used to reduce stress and its impact share acceptance of negative experiences as their core. However, the unique contribution of acceptance remains unclear. The current project will therefore examine the efficacy, mediators, and moderators of a brief acceptance intervention in a student population.

Method: First, data from a pilot study will be explored. Then, data from a randomized trial will be analysed, wherein participants (n = 116) were allocated to the intervention or to psychoeducation about acceptance and stress. They were measured before and after this. The main outcome was psychological well-being. Mediator analyses were conducted with acceptance and interoceptive awareness, and moderator analyses included adherence to home practice, neuroticism, and experience with mindfulness.

Results: In the randomized trial, the interaction effect between condition and time was significant for psychological well-being (d = 0.56, p = 0.003), but not for secondary outcomes. Estimates of the mediating effect of acceptance (-0.63, 95% CI [-1.48, 0.00]) and interoceptive awareness (-0.30, 95% CI [-0.99, 0.26]) did not reach statistical significance. Moderator analyses did not yield significant moderators.

Discussion: Our brief acceptance intervention increased participants’ psychological well-being with a medium effect size. The pilot study confirmed our conceptualization of the intervention, but mediator analysis failed to confirm acceptance as a working mechanism. Limitations include the short timeframe of our study and the conceptual overlap between acceptance and mindfulness. Despite this, the study underlines the importance of acceptance and provides a short and focused intervention.


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