Automated Verification and Optimization of Concurrent Data Structures in C++ using Hybrid Symbolic Execution and Machine Learning

**Abstract:** This paper introduces a novel framework for automated verification and optimization of concurrent data structures in C++, leveraging a hybrid approach combining symbolic execution and reinforcement learning (RL). Traditional verification methods are often impractical for complex concurrent systems due to state-space explosion. Existing static analysis tools may produce false positives, hindering their effectiveness. Our system, "Concurrent Sentinel," addresses these limitations by dynamically narrowing the search space using symbolic execution guided by an RL agent trained to prioritize critical concurrent access patterns. This allows for efficient detection of data races, deadlocks, and other concurrency errors, while simultaneously optimizing performance through automated lock granularity adjustments. The system aims to significantly accelerate the development and deployment of reliable and efficient concurrent C++ applications.