Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions
Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto
摘要
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.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper1
问问它们各自怎么用它它引用的顶会 Paper11
- Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code GenerationJiawei Liu, Chunqiu Steven Xia, Yuyao Wang, Lingming ZhangNeurIPS 2023 · 被引用 2,317 次
- Teaching Large Language Models to Self-DebugXinyun Chen, Maxwell Lin, Nathanael Schärli, Denny ZhouICLR 2024 · 被引用 1,085 次
- DS-1000: A Natural and Reliable Benchmark for Data Science Code GenerationYuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang 等ICML 2023 · 被引用 504 次
- Automated Program Repair in the Era of Large Pre-trained Language ModelsChunqiu Steven Xia, Yuxiang Wei, Lingming ZhangICSE 2023 · 被引用 321 次
- An extensive study on pre-trained models for program understanding and generationZhengran Zeng, Hanzhuo Tan, Haotian Zhang, Jing Li 等ISSTA 2022 · 被引用 142 次
相关 Paper
- "My productivity is boosted, but ..." Demystifying Users' Perception on AI Coding AssistantsYunbo Lyu, Zhou Yang, Jieke Shi, Jianming Chang 等ASE 2025 · 被引用 8 次
- A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and ChallengesJenny T. Liang, Chenyang Yang, Brad A. MyersICSE 2024 · 被引用 126 次
- An Empirical Study of Knowledge Transfer in AI Pair ProgrammingAlisa Welter, Niklas Schneider, Tobias Dick, Kallistos Weis 等ASE 2025
- Code with Me or for Me? How Increasing AI Automation Transforms Developer WorkflowsValerie Chen, Ameet Talwalkar, Robert Brennan, Graham NeubigCHI 2026 · 被引用 2 次
- Using AI Assistants in Software Development: A Qualitative Study on Security Practices and ConcernsJan H. Klemmer, Stefan Albert Horstmann, Nikhil Patnaik, Cordelia Ludden 等CCS 2024 · 被引用 14 次
