AI 中文总结
研究针对软件工程问题定位，提出基于测试驱动的IssueExec方法，通过领域知识增强测试表示弥合语义差距，经分层跟踪分析过滤噪声，实验显示该方法性能达最优，集成后能解决更多问题。
AI 中文摘要
问题定位是自动化软件维护中的关键步骤，现有方法因问题描述与代码实现间的抽象差距而面临困难。理论分析表明测试套件可作为需求的可执行代理，平均减少7.73比特熵的定位不确定性。对18个代码库的实证研究验证了这一点。利用测试进行定位面临语义差距和执行跟踪噪声两大挑战。为此提出IssueExec，通过领域知识增强的测试表示弥合语义差距，通过分层跟踪分析过滤噪声。实验表明IssueExec性能达最优，集成到无代理管道中能解决更多问题。
英文摘要
Issue localization, which identifies code locations requiring modification from issue descriptions, is a critical step in automated software maintenance. Existing approaches predominantly attempt to directly align issue descriptions with code elements, yet often struggle due to the inherent abstraction gap between the issue description and code implementation. Seeking alternative signals, our theoretical analysis suggests that test suites can serve as executable proxies for requirements, reducing localization uncertainty by 7.73 bits of entropy on average. A large-scale empirical study on 18 repositories validates this premise: existing tests cover 96.98\% of ground-truth files, and the two-hop pathway yields stronger semantic connectivity than direct matching in 82.4\% of cases. Despite their potential, leveraging tests for localization faces two key challenges: the semantic gap separating issue descriptions from test identifiers, and the substantial noise in execution traces from infrastructure code. To address these, we propose IssueExec, which bridges the semantic gap through domain-knowledge-enhanced test representations and filters noise via hierarchical trace analysis. Experiments on SWE-bench Lite show that IssueExec achieves state-of-the-art performance, improving function-level Recall@1 by 41.57\% over the strongest baseline. When integrated into the Agentless pipeline, IssueExec resolves 17.72\% more issues, demonstrating practical downstream benefits.
DOI：10.1145/3832290
