Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions
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| Format: | Preprint |
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2024
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| _version_ | 1866916124942139392 |
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| author | Xiao, Tao Hata, Hideaki Treude, Christoph Matsumoto, Kenichi |
| author_facet | Xiao, Tao Hata, Hideaki Treude, Christoph Matsumoto, Kenichi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_08967 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions Xiao, Tao Hata, Hideaki Treude, Christoph Matsumoto, Kenichi Software Engineering 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. |
| title | Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2402.08967 |