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Few real-world hybrid systems are amenable to formal verification, due to their complexity and black box components.  Optimization-based falsification &#x2014;a methodology of search-based testing that employs stochastic optimization&#x2014;is thus attracting attention as an alternative quality assurance method. Inspired by the recent work that advocates  coverage  and  exploration  in falsification, we introduce a two-layered optimization framework that uses Monte Carlo tree search (MCTS), a popular machine learning technique with solid mathematical and empirical foundations (e.g., in computer Go). MCTS is used in the upper layer of our framework; it guides the lower layer of local hill-climbing optimization, thus balancing exploration and exploitation in a disciplined manner. We demonstrate the proposed framework through experiments with benchmarks from the automotive domain.

Two-Layered Falsification of Hybrid Systems Guided by Monte Carlo Tree Search