CodeAgent: Autonomous Communicative Agents for Code Review

• URL: http://arxiv.org/abs/2402.02172v5
• Date: Tue, 24 Sep 2024 20:40:43 GMT
• Title: CodeAgent: Autonomous Communicative Agents for Code Review
• Authors: Xunzhu Tang, Kisub Kim, Yewei Song, Cedric Lothritz, Bei Li, Saad Ezzini, Haoye Tian, Jacques Klein, Tegawende F. Bissyande,
• Abstract summary: 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. Results demonstrate CodeAgent's effectiveness, contributing to a new state-of-the-art in code review automation.
• Score: 12.163258651539236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}).

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