RL-Based False Positive Reduction for Rust Static Memory Safety Analysis
arXiv·medium signal
Static analysis tools like Rudra and MirChecker are essential for Rust memory safety but suffer from high false positive rates that erode developer trust. This paper applies reinforcement learning to learn which warnings are genuine, reducing manual review effort without sacrificing true positive recall. As Rust adoption accelerates in safety-critical domains (Linux kernel, automotive, aerospace), automated false positive filtering becomes a direct productivity multiplier for Rust teams.