CodeAgent: Collaborative Agents for Software Engineering
arxiv(2024)
摘要
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
().
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要