CodeAgent: Autonomous Communicative Agents for Code Review
<|reference_start|>CodeAgent: Autonomous Communicative Agents for Code Review: Code review, which aims at ensuring the overall quality and reliability of software, is a cornerstone of software development. Unfortunately, while crucial, Code review is a labor-intensive process that the research community is looking to automate. Existing automated methods rely on single input-output generative models and thus generally struggle to emulate the collaborative nature of code review. This work introduces \tool{}, a novel multi-agent Large Language Model (LLM) system for code review automation. CodeAgent incorporates a supervisory agent, QA-Checker, to ensure that all the agents' contributions address the initial review question. We evaluated CodeAgent on critical code review tasks: (1) detect inconsistencies between code changes and commit messages, (2) identify vulnerability introductions, (3) validate code style adherence, and (4) suggest code revision. The results demonstrate CodeAgent's effectiveness, contributing to a new state-of-the-art in code review automation. Our data and code are publicly available (\url{https://github.com/Code4Agent/codeagent}).<|reference_end|>
@article{tang2024codeagent:,
    title={CodeAgent: Autonomous Communicative Agents for Code Review},
    author={Xunzhu Tang and Kisub Kim and Yewei Song and Cedric Lothritz and Bei
  Li and Saad Ezzini and Haoye Tian and Jacques Klein and Tegawende F.
  Bissyande},
    journal={arXiv preprint arXiv:2402.02172},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02172},
    primaryClass={cs.SE}
}