Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910893958234112 |
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| author | Ma, Yingwei Yang, Qingping Cao, Rongyu Li, Binhua Huang, Fei Li, Yongbin |
| author_facet | Ma, Yingwei Yang, Qingping Cao, Rongyu Li, Binhua Huang, Fei Li, Yongbin |
| contents | This paper presents Alibaba LingmaAgent, a novel Automated Software Engineering method designed to comprehensively understand and utilize whole software repositories for issue resolution. Deployed in TONGYI Lingma, an IDE-based coding assistant developed by Alibaba Cloud, LingmaAgent addresses the limitations of existing LLM-based agents that primarily focus on local code information. Our approach introduces a top-down method to condense critical repository information into a knowledge graph, reducing complexity, and employs a Monte Carlo tree search based strategy enabling agents to explore and understand entire repositories. We guide agents to summarize, analyze, and plan using repository-level knowledge, allowing them to dynamically acquire information and generate patches for real-world GitHub issues. In extensive experiments, LingmaAgent demonstrated significant improvements, achieving an 18.5\% relative improvement on the SWE-bench Lite benchmark compared to SWE-agent. In production deployment and evaluation at Alibaba Cloud, LingmaAgent automatically resolved 16.9\% of in-house issues faced by development engineers, and solved 43.3\% of problems after manual intervention. Additionally, we have open-sourced a Python prototype of LingmaAgent for reference by other industrial developers https://github.com/RepoUnderstander/RepoUnderstander. In fact, LingmaAgent has been used as a developed reference by many subsequently agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01422 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration Ma, Yingwei Yang, Qingping Cao, Rongyu Li, Binhua Huang, Fei Li, Yongbin Software Engineering Computation and Language This paper presents Alibaba LingmaAgent, a novel Automated Software Engineering method designed to comprehensively understand and utilize whole software repositories for issue resolution. Deployed in TONGYI Lingma, an IDE-based coding assistant developed by Alibaba Cloud, LingmaAgent addresses the limitations of existing LLM-based agents that primarily focus on local code information. Our approach introduces a top-down method to condense critical repository information into a knowledge graph, reducing complexity, and employs a Monte Carlo tree search based strategy enabling agents to explore and understand entire repositories. We guide agents to summarize, analyze, and plan using repository-level knowledge, allowing them to dynamically acquire information and generate patches for real-world GitHub issues. In extensive experiments, LingmaAgent demonstrated significant improvements, achieving an 18.5\% relative improvement on the SWE-bench Lite benchmark compared to SWE-agent. In production deployment and evaluation at Alibaba Cloud, LingmaAgent automatically resolved 16.9\% of in-house issues faced by development engineers, and solved 43.3\% of problems after manual intervention. Additionally, we have open-sourced a Python prototype of LingmaAgent for reference by other industrial developers https://github.com/RepoUnderstander/RepoUnderstander. In fact, LingmaAgent has been used as a developed reference by many subsequently agents. |
| title | Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration |
| topic | Software Engineering Computation and Language |
| url | https://arxiv.org/abs/2406.01422 |