Generative AI for pull request descriptions: Adoption, impact, and developer interventions

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...

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Main Authors: XIAO, Tao, HATA, Hideaki, TREUDE, Christoph, MATSUMOTO, Kenichi
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/9174
https://ink.library.smu.edu.sg/context/sis_research/article/10179/viewcontent/3643773__1_.pdf
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spelling sg-smu-ink.sis_research-101792024-08-13T05:30:17Z Generative AI for pull request descriptions: Adoption, impact, and developer interventions XIAO, Tao HATA, Hideaki TREUDE, Christoph MATSUMOTO, Kenichi 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. 2024-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9174 info:doi/10.1145/3643773 https://ink.library.smu.edu.sg/context/sis_research/article/10179/viewcontent/3643773__1_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Pull Requests Generative AI Copilot GitHub Artificial Intelligence and Robotics Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Pull Requests
Generative AI
Copilot
GitHub
Artificial Intelligence and Robotics
Software Engineering
spellingShingle Pull Requests
Generative AI
Copilot
GitHub
Artificial Intelligence and Robotics
Software Engineering
XIAO, Tao
HATA, Hideaki
TREUDE, Christoph
MATSUMOTO, Kenichi
Generative AI for pull request descriptions: Adoption, impact, and developer interventions
description 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 text
author XIAO, Tao
HATA, Hideaki
TREUDE, Christoph
MATSUMOTO, Kenichi
author_facet XIAO, Tao
HATA, Hideaki
TREUDE, Christoph
MATSUMOTO, Kenichi
author_sort XIAO, Tao
title Generative AI for pull request descriptions: Adoption, impact, and developer interventions
title_short Generative AI for pull request descriptions: Adoption, impact, and developer interventions
title_full Generative AI for pull request descriptions: Adoption, impact, and developer interventions
title_fullStr Generative AI for pull request descriptions: Adoption, impact, and developer interventions
title_full_unstemmed Generative AI for pull request descriptions: Adoption, impact, and developer interventions
title_sort generative ai for pull request descriptions: adoption, impact, and developer interventions
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/9174
https://ink.library.smu.edu.sg/context/sis_research/article/10179/viewcontent/3643773__1_.pdf
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