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The integration of machine learning into cyber-physical systems (CPS) promises enhanced efficiency and autonomous capabilities, revolutionizing fields like autonomous vehicles and telemedicine. This evolution necessitates a shift in the software development life cycle, where data and learning are pivotal. Traditional verification and validation methods are inadequate for these AI-driven systems. This study focuses on the challenges in ensuring safety in learning-enabled CPS. It emphasizes the role of testing as a primary method for verification and validation, critiques current methodologies, and advocates for a more rigorous approach to assure formal safety.
Zheng et al. (Wed,) studied this question.