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.

翻译：GitHub的Copilot for Pull Requests（PRs）是一项有前景的服务，旨在自动化与PR相关的各种开发者任务，例如生成更改摘要或提供包含相关代码链接的完整指南。随着这一创新技术在开源软件社区中获得关注，研究其早期采用情况及其对开发过程的影响至关重要。此外，这为观察开发者在不同意生成内容时的应对方式提供了独特契机。本研究采用混合方法，将定量分析与定性洞察相结合，对18,256个由生成式AI编写部分描述的PRs进行了分析。研究结果表明：（1）Copilot for PRs虽处于早期阶段，但其采用率显著上升；（2）经Copilot for PRs优化的PRs所需审查时间更短，且合并可能性更高；（3）使用Copilot for PRs的开发者常以手动输入补充自动生成的描述。这些结果为了解生成式AI在软件开发中的日益整合提供了宝贵见解。