Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration

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 developersthis https URL. In fact, LingmaAgent has been used as a developed reference by many subsequently agents.

@article{ma2025_2406.01422,
  title={ Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration },
  author={ Yingwei Ma and Qingping Yang and Rongyu Cao and Binhua Li and Fei Huang and Yongbin Li },
  journal={arXiv preprint arXiv:2406.01422},
  year={ 2025 }
}