Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions
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Abstract (arXiv)
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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Feb 2024.
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Citing papers per year: 2024: 1; 2025: 3; 2026: 6.
10 papers.
- 2024Automatic Programming: Large Language Models and Beyondcited by 35
- 2025How Developers Interact with AI: A Taxonomy of Human-AI Collaboration in Software Engineeringcited by 14
- 2026Human-AI Synergy in Agentic Code Reviewcited by 3
- 2026Just-in-Time Catching Test Generation at Metacited by 1
- 2026Rethinking Code Review in the Age of AI: A Vision for Agentic Code Reviewcited by 1
- 2025AI and Agile Software Development: A Research Roadmap from the XP2025 Workshopcited by 0
- 2025A Human Behavioral Baseline for Collective Governance in Software Projectscited by 0
- 2026DepRadar: Agentic Coordination for Context Aware Defect Impact Analysis in Deep Learning Librariescited by 0
- 2026From Conversation to Contribution: Characterizing Coding Agent in Open-Source Softwarecited by 0
- 2026ExplainBench: Evaluating Code Explanations from Agentscited by 0
- 2026ExplainBench: Evaluating Code Explanations from Agents29 Jul 2026
- 2026From Conversation to Contribution: Characterizing Coding Agent in Open-Source Software6 Jul 2026
- 2026Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review17 May 2026
- 2026Human-AI Synergy in Agentic Code Review16 Mar 2026
- 2026Just-in-Time Catching Test Generation at Meta30 Jan 2026
- 2026DepRadar: Agentic Coordination for Context Aware Defect Impact Analysis in Deep Learning Libraries14 Jan 2026
- 2025A Human Behavioral Baseline for Collective Governance in Software Projects10 Oct 2025
- 2025AI and Agile Software Development: A Research Roadmap from the XP2025 Workshop28 Aug 2025
- 2025How Developers Interact with AI: A Taxonomy of Human-AI Collaboration in Software Engineering15 Jan 2025
- 2024Automatic Programming: Large Language Models and Beyond3 May 2024