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.

翻译：近年来，大语言模型（LLM）智能体的发展正在彻底改变自主软件工程（ASE），实现了自动化编码、问题修复和功能改进。然而，定位——即通过导航到相关代码段来精确识别软件问题——仍然是一个重大挑战。由于LLM智能体与精确代码搜索机制之间缺乏有效集成，现有方法通常产生次优结果。本文介绍了OrcaLoca，一个LLM智能体框架，它通过集成基于优先级的LLM引导动作调度、带有相关性评分的动作分解以及距离感知的上下文剪枝，提高了软件问题定位的准确性。实验结果表明，在SWE-bench Lite基准测试中，OrcaLoca在函数匹配率（65.33%）上成为新的开源最优方法（SOTA）。通过其补丁生成集成，该框架还将一个开源框架的最终解决率提高了6.33个百分点。