Counterexample Guided Repair

Counterexample-guided repair (CGR) focuses on automatically fixing errors or flaws in various systems, ranging from software and robotic control programs to deep neural networks. Current research emphasizes developing efficient algorithms that leverage counterexamples—instances where the system fails—to iteratively refine its behavior, often employing techniques like gradient-based optimization or search-based methods to make targeted adjustments. This approach is crucial for enhancing the reliability and safety of complex systems, particularly in safety-critical applications where manual debugging is impractical or impossible, and is driving advancements in areas like formal verification and trustworthy AI.