Project-Level Resource Leak Detection through Agent-based Ownership Analysis and Repair Pattern Verification

Resource leaks cause system performance degradation and crashes. Traditional static analysis approaches rely on predefined rules, limiting their ability to find unknown leaks. Emerging LLM-based methods can locate APIs but face challenges in large-scale projects, including high costs and numerous false positives. To address these limitations, we propose AROP, the first project-level resource leak detection framework integrating LLM-based agents. AROP first extracts project-level metadata to efficiently filter resource-related files. It then employs a detection agent to identify acquisition/release operations, followed by a validation agent and data-flow dependency analysis to reduce false positives. Finally, it determines leaks through leak repair pattern analysis.