Learning-Based Falsification for Model Families of Cyber-Physical Systems
Koki Kato, Fuyuki Ishikawa · 2019
Verification of cyber-physical systems is challenging with the continuous dynamics of increasing complexity. Falsification has been considered as a promising pragmatic approach by using optimization techniques to search for input signals that lead to violation of a quantified formal specification. However, current falsification methods run a search every time from scratch given any variation in the target model. We propose a learning-based method that builds a falsifier for a family of models with a preliminary learning process. We adapt reinforcement learning techniques for this purpose. We evaluated the performance of the proposed method with a major falsification tool.