OrcaLoca: An LLM Agent Framework for Software Issue Localization
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917000322744320 |
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| author | Yu, Zhongming Zhang, Hejia Zhao, Yujie Huang, Hanxian Yao, Matrix Ding, Ke Zhao, Jishen |
| author_facet | Yu, Zhongming Zhang, Hejia Zhao, Yujie Huang, Hanxian Yao, Matrix Ding, Ke Zhao, Jishen |
| contents | Recent developments in Large Language Model (LLM) agents are revolutionizing Autonomous Software Engineering (ASE), enabling automated coding, problem fixes, and feature improvements. However, localization -- precisely identifying software problems by navigating to relevant code sections -- remains a significant challenge. Current approaches often yield suboptimal results due to a lack of effective integration between LLM agents and precise code search mechanisms. This paper introduces OrcaLoca, an LLM agent framework that improves accuracy for software issue localization by integrating priority-based scheduling for LLM-guided action, action decomposition with relevance scoring, and distance-aware context pruning. Experimental results demonstrate that OrcaLoca becomes the new open-source state-of-the-art (SOTA) in function match rate (65.33%) on SWE-bench Lite. It also improves the final resolved rate of an open-source framework by 6.33 percentage points through its patch generation integration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00350 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OrcaLoca: An LLM Agent Framework for Software Issue Localization Yu, Zhongming Zhang, Hejia Zhao, Yujie Huang, Hanxian Yao, Matrix Ding, Ke Zhao, Jishen Software Engineering Artificial Intelligence Recent developments in Large Language Model (LLM) agents are revolutionizing Autonomous Software Engineering (ASE), enabling automated coding, problem fixes, and feature improvements. However, localization -- precisely identifying software problems by navigating to relevant code sections -- remains a significant challenge. Current approaches often yield suboptimal results due to a lack of effective integration between LLM agents and precise code search mechanisms. This paper introduces OrcaLoca, an LLM agent framework that improves accuracy for software issue localization by integrating priority-based scheduling for LLM-guided action, action decomposition with relevance scoring, and distance-aware context pruning. Experimental results demonstrate that OrcaLoca becomes the new open-source state-of-the-art (SOTA) in function match rate (65.33%) on SWE-bench Lite. It also improves the final resolved rate of an open-source framework by 6.33 percentage points through its patch generation integration. |
| title | OrcaLoca: An LLM Agent Framework for Software Issue Localization |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2502.00350 |