Master's Thesis in Software Development: Investigating the quality of AI-generated commit Messages

<p><strong></strong></p><p><strong>Commit messages shape how teams understand and maintain their code, but AI tools are starting to take over this task. In this thesis, you analyze the quality of AI-generated commit messages and compare them to human-written ones. The goal: uncover how well these tools capture the intent behind code changes and where they still fall short.</strong></p><p></p><p><strong><strong><strong><strong>💡</strong></strong></strong>Areas of Interest:&nbsp;</strong>AI-augmented software development, developer workflows, version control systems</p>

<p></p><p>Commit messages are a vital part of modern software development. They help teams track changes, understand decisions and maintain codebases over time. More and more, AI tools like GitHub Copilot generate these messages automatically. While this can speed up workflows, it’s unclear how this shift affects message quality. AI may summarize code effectively but often lacks the context behind decisions. To evaluate this development, we need a clearer picture of what makes a commit message effective and how AI compares to humans in practice.</p><p></p><h2>The Assignment</h2><p></p><p>In this thesis, you will contribute to improving commit message quality by exploring how AI can support this part of the development workflow. The goal is to understand how AI-generated commit messages compare to human-written ones in terms of clarity, relevance and usefulness for understanding code changes. Based on your findings, you will draw conclusions about the strengths and limitations of current AI-generated commit messages and identify opportunities for improvement.</p><p></p><ul>
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