Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration
Yingwei Ma, Qingping Yang, Rongyu Cao, Binhua Li, Fei, Huang, Yongbin Li

TL;DR
Alibaba LingmaAgent is a novel automated issue resolution method that leverages comprehensive repository exploration and knowledge graphs, significantly improving accuracy and efficiency in real-world software engineering tasks.
Contribution
The paper introduces LingmaAgent, a new approach that uses repository-level knowledge graphs and Monte Carlo tree search to enhance automated issue resolution beyond local code analysis.
Findings
Achieved 18.5% improvement on SWE-bench Lite benchmark.
Automatically resolved 16.9% of in-house issues at Alibaba Cloud.
Solved 43.3% of problems after manual intervention.
Abstract
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,…
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Taxonomy
TopicsSoftware Engineering Research
MethodsFocus
