CodeAgent: Autonomous Communicative Agents for Code Review
Fuente:
arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916410407518208 |
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| author | Tang, Xunzhu Kim, Kisub Song, Yewei Lothritz, Cedric Li, Bei Ezzini, Saad Tian, Haoye Klein, Jacques Bissyande, Tegawende F. |
| author_facet | Tang, Xunzhu Kim, Kisub Song, Yewei Lothritz, Cedric Li, Bei Ezzini, Saad Tian, Haoye Klein, Jacques Bissyande, Tegawende F. |
| contents | 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}). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02172 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CodeAgent: Autonomous Communicative Agents for Code Review Tang, Xunzhu Kim, Kisub Song, Yewei Lothritz, Cedric Li, Bei Ezzini, Saad Tian, Haoye Klein, Jacques Bissyande, Tegawende F. Software Engineering 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}). |
| title | CodeAgent: Autonomous Communicative Agents for Code Review |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2402.02172 |