Developers and Generative AI: A Study of Self-Admitted Usage in Open Source Projects
This provides insights for developers on integrating AI into workflows and for researchers on automation tasks, but it is incremental as it builds on prior work.
The study investigated how developers use generative AI tools like ChatGPT and GitHub Copilot in open-source projects by mining GitHub artifacts, resulting in a taxonomy of 64 usage tasks across 7 categories and showing expanded usage avenues over time.
The availability of generative Artificial Intelligence (AI) tools such as ChatGPT or GitHub Copilot is reshaping the way in which software is developed, evolved, and maintained. Oftentimes, developers leave traces of such an usage in software artifacts. This allows not only to understand how AI is used in software development, but also to let others be aware how such software artifacts were created, e.g., for licensing or trustworthiness purposes. This paper-building upon our preliminary work presented at MSR 2024-aims at qualitatively investigating on the self-admitted use of two very popular generative AI tools - ChatGPT and GitHub Copilot - in software development. To this aim, we mined GitHub for such traces, by looking at commits, issues and pull requests (PRs). Then, through a manual coding, we create a taxonomy of 64 different ChatGPT and GitHub Copilot usage tasks, grouped into 7 categories. By repeating our previous analysis two years after and by extending it to GitHub Copilot, we show how the usage avenues have been expanded, the extent to which developers perceived such a generative AI usage useful, and whether some concerns occurring more than one year ago are no longer present. The taxonomy of tasks we derived from such a qualitative study provided (i) developers with valuable insights into how generative AI can be integrated into their workflows, and (ii) researchers with a clear overview of tasks that developers perceive as well-suited for automation.