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Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions

Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto

TL;DR

This study analyzes the early adoption and impact of GitHub's Copilot for PRs using over 18k AI-generated PRs and 54k non-AI PRs to quantify adoption, review efficiency, and merge likelihood. Through quantitative analyses and causal inference with Entropy Balancing, the authors show that Copilot-generated PR descriptions shorten review times by about 19.3 hours and increase merge probability by ~1.57×, while developers frequently augment or modify AI content. A rich qualitative analysis reveals 13 categories of supplementary information and seven types of editorial actions, with static templates and linked references being the most common augmentations. The work provides practical recommendations for integrating Copilot for PRs with PR templates and highlights the importance of human-in-the-loop refinement in AI-assisted software development, while noting threats to validity related to early adopters and potential coding biases.

Abstract

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.

Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions

TL;DR

This study analyzes the early adoption and impact of GitHub's Copilot for PRs using over 18k AI-generated PRs and 54k non-AI PRs to quantify adoption, review efficiency, and merge likelihood. Through quantitative analyses and causal inference with Entropy Balancing, the authors show that Copilot-generated PR descriptions shorten review times by about 19.3 hours and increase merge probability by ~1.57×, while developers frequently augment or modify AI content. A rich qualitative analysis reveals 13 categories of supplementary information and seven types of editorial actions, with static templates and linked references being the most common augmentations. The work provides practical recommendations for integrating Copilot for PRs with PR templates and highlights the importance of human-in-the-loop refinement in AI-assisted software development, while noting threats to validity related to early adopters and potential coding biases.

Abstract

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.
Paper Structure (20 sections, 4 figures, 6 tables)

This paper contains 20 sections, 4 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Copilot for PRs
  4. Related Work
  5. Data Collection
  6. PRs Generated by Copilot for PRs
  7. PRs Not generated by Copilot for PRs
  8. Revisions of PR Descriptions Generated by Copilot for PRs
  9. Methods
  10. Quantitative Analysis
  11. Casual Inference
  12. Qualitative Analysis
  13. Results
  14. RQ1: To what extent do developers use Copilot for PRs in the code review process?
  15. RQ2: How are the code reviews affected by the use of Copilot for PRs?
  16. ...and 5 more sections

Figures (4)

  • Figure 1: The GitHub Copilot for PRs feature: an example of copilot:all.
  • Figure 2: Covariate balance before (unadjusted) and after (adjusted) propensity score weighting.
  • Figure 3: Cumulative time-series of PRs using Copilot for PRs vs. Non-Copilot for PRs.
  • Figure 4: Proportions of PRs using Copilot for PRs per repository.