Unity Is Strength: Collaborative LLM-Based Agents for Code Reviewer Recommendation

Luqiao Wang, Yangtao Zhou, Huiying Zhuang, Qingshan Li, Di Cui, Yutong Zhao, Lu Wang

Abstract

Assigning pull requests to appropriate code reviewers can accelerate the review process and help uncover potential bugs. However, the inherent complexities in pull requests and code reviewers present challenges in making suitable matches between them. Prior studies focus on mining rich semantic information from pull requests or profile information from code reviewers to improve efficiency. These approaches often overlook the intrinsic relationships between pull requests and code reviewers, which can be represented by a combination of multiple factors and strategies, resulting in suboptimal recommendation accuracy.

BibTeX

@inproceedings{Wang-al:ASE24,
  author    = {Luqiao Wang and
               Yangtao Zhou and
               Huiying Zhuang and
               Qingshan Li and
               Di Cui and
               Yutong Zhao and
               Lu Wang},
  title     = {Unity Is Strength: Collaborative {LLM-Based} Agents for Code Reviewer Recommendation},
  booktitle = {ASE},
  pages     = {2235--2239},
  publisher = {{ACM}},
  year      = {2024},
}

Unity Is Strength: Collaborative LLM-Based Agents for Code Reviewer Recommendation

Abstract

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