Symbolic Execution

Symbolic execution is a technique for analyzing program behavior by systematically exploring all possible execution paths, representing program states symbolically rather than concretely. Current research focuses on integrating symbolic execution with neural networks (neurosymbolic AI) to improve the reliability and interpretability of AI systems, particularly in areas like code generation, vulnerability detection, and program verification. This approach enhances the ability to reason about complex systems, leading to more robust and trustworthy AI, improved software security, and more efficient program optimization. The development of efficient symbolic execution methods and their integration with machine learning techniques are driving significant advancements in various fields.