IssueExec: A Test-Driven Approach for Localizing Software Engineering Issues

2026-07-19

AI Summary

This work addresses the semantic gap between problem descriptions and code implementations in software engineering fault localization by proposing a test-driven localization approach. It systematically demonstrates, for the first time, that test cases substantially reduce localization uncertainty and innovatively integrates domain knowledge to enrich test semantic representations. A hierarchical execution trace analysis mechanism is designed to effectively bridge the semantic discrepancy between requirements and code while suppressing noise. Evaluated on SWE-bench Lite, the method achieves a 41.57% relative improvement in function-level Recall@1 over the strongest baseline. When integrated into the Agentless pipeline, it resolves 17.72% more problems than the original framework.

Abstract

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.

Problem

• issue localization
• software maintenance
• test-driven
• semantic gap
• code localization

Innovation

• test-driven localization
• semantic gap bridging
• hierarchical trace analysis
• executable requirements
• issue localization