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 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.
Thu 18 JulDisplayed time zone: Brasilia, Distrito Federal, Brazil change
14:00 - 15:30 | Software Maintenance and Comprehension 3Research Papers / Journal First at Pitomba Chair(s): Xin Xia Huawei Technologies | ||
14:00 18mTalk | Revealing Software Development Work Patterns with PR-Issue Graph Topologies Research Papers Cleidson de Souza Federal University of Pará, Brazil, Emilie Ma University of British Columbia, Jesse Wong University of British Columbia, Dongwook Yoon University of British Columbia, Ivan Beschastnikh University of British Columbia | ||
14:18 18mTalk | Using acceptance tests to predict merge conflict risk Journal First Thaís Rocha UFAPE - Universidade Federal do Agreste de Pernambuco, Paulo Borba Federal University of Pernambuco Pre-print | ||
14:36 18mTalk | Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions Research Papers Tao Xiao Nara Institute of Science and Technology, Hideaki Hata Shinshu University, Christoph Treude Singapore Management University, Kenichi Matsumoto Nara Institute of Science and Technology Pre-print Media Attached | ||
14:54 18mTalk | SimLLM: Measuring Semantic Similarity in Code Summaries Using a Large Language Model-Based Approach Research Papers | ||
15:12 18mTalk | Sharing Software-Evolution Datasets: Practices, Challenges, and Recommendations Research Papers David Broneske DZHW Hannover, Germany, Sebastian Kittan Otto-von-Guericke Unviersity Magdeburg, Germany, Jacob Krüger Eindhoven University of Technology |