The rise of AI-assisted coding tools like GitHub Copilot and OpenAI’s Codex has significantly increased the efficiency of the coding process. Developers can now generate code snippets, functions, and even entire modules with less effort and time. This acceleration not only enhances productivity but also lowers the barrier for contributing to open-source projects.
Hypothesis 1: With AI-assisted coding making it easier to write code, more people—including less experienced developers—are contributing to open-source repositories.
However, the influx of contributions presents new challenges. The number of experienced maintainers and reviewers responsible for overseeing these projects has not scaled proportionally. These experts are tasked with reviewing submissions, ensuring code quality, and integrating changes.
Hypothesis 2: The increased volume of contributions overburdens maintainers, potentially leading to less thorough reviews and a decline in overall code quality.
Hypothesis 3: The bottleneck in the review process may slow down development cycles due to the backlog of contributions awaiting approval.
Understanding these dynamics is crucial for the sustainability of open-source communities. It raises important questions about code quality, project governance, contributor management, and the evolution of contribution policies and documentation.
This thesis aims to explore the impact of AI-assisted coding on open-source software development. Depending on your interests, the thesis can take one or more of the following directions:
1. Analyzing Contribution Trends in Open-Source Projects
Focus: Investigate whether AI-assisted coding tools have led to an increase in the number of contributions to open-source repositories.
Objectives:
• Collect and analyze data from various open-source projects to identify trends in contribution rates over time.
• Determine if there is a correlation between the introduction of AI coding tools and the volume of contributions.
• Assess the skill levels of contributors to see if more novice developers are participating.
2. Assessing the Impact on Code Quality
Focus: Evaluate whether the quality of code in open-source projects has been affected by the increased use of AI-assisted coding.
Objectives:
• Examine metrics such as code review times, rejection rates, bug frequency, and post-merge defects.
• Analyze whether maintainers are experiencing difficulties in keeping up with the review process.
• Investigate if the thoroughness of code reviews has diminished due to increased workload.
3. Investigating Development Cycle Efficiency
Focus: Explore how the surge in contributions impacts the speed and efficiency of development cycles in open-source projects.
Objectives:
• Measure the time from code submission to merge or rejection before and after the widespread adoption of AI-assisted coding.
• Identify any delays or bottlenecks in the development process.
• Evaluate the maintainers’ capacity to handle increased workloads.
4. Analyzing Contribution Policies and Documentation
Focus: Examine how different open-source projects manage the influx of contributions through their policies and guidelines.
Objectives:
• Review and compare contribution guidelines across various repositories.
• Identify best practices and strategies for managing large volumes of contributions.
• Determine if projects with more detailed policies experience different outcomes in terms of code quality and development speed.
5. Providing Recommendations for Open-Source Communities
Focus: Offer insights and practical recommendations to help open-source projects adapt to the challenges posed by AI-assisted coding.
Objectives:
• Develop strategies for maintainers to manage increased contributions without compromising code quality.
• Suggest improvements to contribution policies and documentation.
• Propose tools or processes that could alleviate the burden on maintainers.
By conducting a structured literature review and empirical analysis, you will contribute valuable knowledge to the field of software engineering and open-source community management.
• Passion for Software Development and Open-Source: A strong interest in coding, open-source projects, and community dynamics.
• Understanding of AI in Coding: Familiarity with AI-assisted coding tools and their impact on the development process.
• Research Skills: Proficiency in conducting literature reviews, data collection, and statistical analysis.
• Analytical Thinking: Ability to critically analyze data and identify patterns and trends.
• Self-Motivation and Independence: Capable of working independently, managing time effectively, and driving the project forward.
• Expert Supervision: Guidance from researchers at the institute.
• Collaborative Environment: Opportunities to engage with a research community and contribute to ongoing projects.
• Flexible Start Date: The option to begin the thesis at a time that suits you.
• Academic and Practical Impact: The potential to publish your findings and make meaningful contributions to open-source communities.
If you are eager to explore the intersection of AI-assisted coding and open-source software development, please submit:
• Your CV: Detailing relevant experience, skills, and academic background.
• Academic Transcripts: Providing records of your coursework and grades.
• Motivation Letter: A brief statement (max. 200 words) explaining your interest in this topic and how your background makes you a suitable candidate.
• Proposed Timeline: An outline of your planned approach to the thesis and how you intend to manage your time throughout the project.