CodeAgent: Autonomous Communicative Agents for Code Review

Xunzhu Tang, Kisub Kim, Yewei Song, Cedric Lothritz, Bei Li, Saad Ezzini, Haoye Tian, Jacques Klein, Tegawendé F. Bissyandé


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).
Anthology ID:
2024.emnlp-main.632
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11279–11313
Language:
URL:
https://aclanthology.org/2024.emnlp-main.632
DOI:
10.18653/v1/2024.emnlp-main.632
Bibkey:
Cite (ACL):
Xunzhu Tang, Kisub Kim, Yewei Song, Cedric Lothritz, Bei Li, Saad Ezzini, Haoye Tian, Jacques Klein, and Tegawendé F. Bissyandé. 2024. CodeAgent: Autonomous Communicative Agents for Code Review. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 11279–11313, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
CodeAgent: Autonomous Communicative Agents for Code Review (Tang et al., EMNLP 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2024.emnlp-main.632.pdf
Software:
 2024.emnlp-main.632.software.zip
Data:
 2024.emnlp-main.632.data.zip