Minimizing False Positives in Static Bug Detection via LLM-Enhanced Path Feasibility Analysis
🤖 AI Summary
Static error detection in large codebases suffers from high false-positive rates due to insufficient path feasibility analysis—especially for multi-branch and interprocedural complex dependencies. To address this, we propose LLM4PFA, the first agent-planning-driven, context-aware path feasibility analysis framework. It integrates LLM-based agents, constraint-cascading reasoning, dynamic context extraction, and iterative symbolic pruning to precisely identify infeasible paths. Unlike conventional static analyzers, LLM4PFA overcomes fundamental limitations in constraint propagation and scalability. Evaluated on real-world projects, LLM4PFA reduces false positives by 72%–96%, outperforming the best baseline by 41.1%–105.7%. It achieves high precision with only three missed true vulnerabilities among 45 confirmed positives. This substantial improvement enhances both the accuracy and practical utility of vulnerability detection in industrial-scale codebases.
Application Category
📝 Abstract
Static bug analyzers play a crucial role in ensuring software quality. However, existing analyzers for bug detection in large codebases often suffer from high false positive rates. This is primarily due to the limited capabilities of analyzers in path feasibility validation with multiple conditional branches and complex data dependencies. While current LLM-based approaches attempt to address this issue, their effectiveness remains limited due to insufficient constraint cascade analysis and scalability challenges in large projects. To address this challenge, we propose an iterative path feasibility analysis framework LLM4PFA. By leveraging LLM agent based targeted constraint reasoning, and key context-aware analysis driven by agent planning, LLM4PFA effectively enhances complex inter-procedural path feasibility analysis for minimizing false positives in static bug detection. Evaluation results show that LLM4PFA precisely filters out 72% to 96% false positives reported during static bug detection, significantly outperforming all the baselines by 41.1% - 105.7% improvements; meanwhile LLM4PFA only misses 3 real bugs of 45 true positives.
Problem
Research questions and friction points this paper is trying to address.
Reducing false positives in static bug detection
Enhancing path feasibility analysis with LLMs
Improving scalability for large codebases
Innovation
Methods, ideas, or system contributions that make the work stand out.
LLM-enhanced path feasibility analysis framework
Agent-based targeted constraint reasoning
Context-aware inter-procedural analysis
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