Agents as Neuro-Symbolic Reasoners: Path Feasibility Reasoning for Precise Static Bug Detection

Static bug analyzers play a crucial role in ensuring software quality. However, existing analyzers for bug detection in large-scale codebases often suffer from high false positive rates. This limitation largely stems from their inadequate capabilities in performing precise path feasibility validation for complex code contexts. While recent work has explored Large Language Models (LLMs) to eliminate false positives in static bug detection, the limited reasoning capabilities of LLMs over long and complex contexts hinder their effectiveness when applied to large-scale software projects. To address this challenge, we propose PathAgent, an agent-driven framework for fine-grained path feasibility analysis. PathAgent decomposes complex inter-procedural analysis into localized symbolic range reasoning sub-tasks, and employs an on-demand contextual exploration strategy to adaptively retrieve only the necessary code context. These designs enable precise path feasibility reasoning and effectively reduce false positives reported by static bug analyzers. Evaluation on large-scale real-world projects shows that PathAgent eliminates 79% of false positives, outperforming all baselines by 23% to 44%, while maintaining strong bug detection capability with a recall of 0.94.