Generative AI for Pull Request Descriptions: Adoption, Impact, and
Developer Interventions
- URL: http://arxiv.org/abs/2402.08967v1
- Date: Wed, 14 Feb 2024 06:20:57 GMT
- Title: Generative AI for Pull Request Descriptions: Adoption, Impact, and
Developer Interventions
- Authors: Tao Xiao, Hideaki Hata, Christoph Treude, and Kenichi Matsumoto
- Abstract summary: GitHub's Copilot for Pull Requests (PRs) is a promising service aiming to automate various developer tasks related to PRs.
In this study, we examine 18,256 PRs in which parts of the descriptions were crafted by generative AI.
Our findings indicate that Copilot for PRs, though in its infancy, is seeing a marked uptick in adoption.
- Score: 11.620351603683496
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: GitHub's Copilot for Pull Requests (PRs) is a promising service aiming to
automate various developer tasks related to PRs, such as generating summaries
of changes or providing complete walkthroughs with links to the relevant code.
As this innovative technology gains traction in the Open Source Software (OSS)
community, it is crucial to examine its early adoption and its impact on the
development process. Additionally, it offers a unique opportunity to observe
how developers respond when they disagree with the generated content. In our
study, we employ a mixed-methods approach, blending quantitative analysis with
qualitative insights, to examine 18,256 PRs in which parts of the descriptions
were crafted by generative AI. Our findings indicate that: (1) Copilot for PRs,
though in its infancy, is seeing a marked uptick in adoption. (2) PRs enhanced
by Copilot for PRs require less review time and have a higher likelihood of
being merged. (3) Developers using Copilot for PRs often complement the
automated descriptions with their manual input. These results offer valuable
insights into the growing integration of generative AI in software development.
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