Abstract :
[en] 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}).
Scopus citations®
without self-citations
3