CodeAgent: Autonomous Communicative Agents for Code Review
Xunzhu Tang, Kisub Kim, Yewei Song, Cedric Lothritz, Bei Li, Saad Ezzini, Haoye Tian, Jacques Klein
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 CodeAgent, 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 revisions. 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 (https://github.com/Daniel4SE/codeagent).
BibTeX
@inproceedings{tang-etal-2024-codeagent,
title = "{C}ode{A}gent: Autonomous Communicative Agents for Code Review",
author = "Tang, Xunzhu and
Kim, Kisub and
Song, Yewei and
Lothritz, Cedric and
Li, Bei and
Ezzini, Saad and
Tian, Haoye and
Klein, Jacques and
Bissyand{\'e}, Tegawend{\'e} F.",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.632/",
doi = "10.18653/v1/2024.emnlp-main.632",
pages = "11279--11313"
}