UniDebugger: Hierarchical Multi-Agent Framework for Unified Software Debugging
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arXiv
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| Format: | Preprint |
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2024
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| author | Lee, Cheryl Xia, Chunqiu Steven Yang, Longji Huang, Jen-tse Zhu, Zhouruixin Zhang, Lingming Lyu, Michael R. |
| author_facet | Lee, Cheryl Xia, Chunqiu Steven Yang, Longji Huang, Jen-tse Zhu, Zhouruixin Zhang, Lingming Lyu, Michael R. |
| contents | Software debugging is a time-consuming endeavor involving a series of steps, such as fault localization and patch generation, each requiring thorough analysis and a deep understanding of the underlying logic. While large language models (LLMs) demonstrate promising potential in coding tasks, their performance in debugging remains limited. Current LLM-based methods often focus on isolated steps and struggle with complex bugs. In this paper, we propose the first end-to-end framework, FixAgent, for unified debugging through multi-agent synergy. It mimics the entire cognitive processes of developers, with each agent specialized as a particular component of this process rather than mirroring the actions of an independent expert as in previous multi-agent systems. Agents are coordinated through a three-level design, following a cognitive model of debugging, allowing adaptive handling of bugs with varying complexities. Experiments on extensive benchmarks demonstrate that FixAgent significantly outperforms state-of-the-art repair methods, fixing 1.25$\times$ to 2.56$\times$ bugs on the repo-level benchmark, Defects4J. This performance is achieved without requiring ground-truth root-cause code statements, unlike the baselines. Our source code is available on https://github.com/AcceptePapier/UniDebugger. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17153 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UniDebugger: Hierarchical Multi-Agent Framework for Unified Software Debugging Lee, Cheryl Xia, Chunqiu Steven Yang, Longji Huang, Jen-tse Zhu, Zhouruixin Zhang, Lingming Lyu, Michael R. Software Engineering Software debugging is a time-consuming endeavor involving a series of steps, such as fault localization and patch generation, each requiring thorough analysis and a deep understanding of the underlying logic. While large language models (LLMs) demonstrate promising potential in coding tasks, their performance in debugging remains limited. Current LLM-based methods often focus on isolated steps and struggle with complex bugs. In this paper, we propose the first end-to-end framework, FixAgent, for unified debugging through multi-agent synergy. It mimics the entire cognitive processes of developers, with each agent specialized as a particular component of this process rather than mirroring the actions of an independent expert as in previous multi-agent systems. Agents are coordinated through a three-level design, following a cognitive model of debugging, allowing adaptive handling of bugs with varying complexities. Experiments on extensive benchmarks demonstrate that FixAgent significantly outperforms state-of-the-art repair methods, fixing 1.25$\times$ to 2.56$\times$ bugs on the repo-level benchmark, Defects4J. This performance is achieved without requiring ground-truth root-cause code statements, unlike the baselines. Our source code is available on https://github.com/AcceptePapier/UniDebugger. |
| title | UniDebugger: Hierarchical Multi-Agent Framework for Unified Software Debugging |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2404.17153 |