Test Synthesis

Test synthesis focuses on automatically generating test cases to rigorously evaluate the behavior of systems, particularly complex ones like autonomous vehicles or machine learning models. Current research emphasizes techniques like reinforcement learning (e.g., Monte Carlo Tree Search), game theory, and optimization methods (e.g., mixed-integer linear programming) to synthesize tests that effectively explore system behavior, often guided by formal specifications (e.g., temporal logic) or coverage criteria. This work is crucial for ensuring the reliability and safety of increasingly sophisticated systems across various domains, improving confidence in their functionality and identifying potential vulnerabilities before deployment.