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For exhaustive formal verification, industrial-scale cyber-physical systems(CPSs) are often too large and complex, and lightweight alternatives (e.g.,monitoring and testing) have attracted the attention of both industrialpractitioners and academic researchers. Falsification is one popular testingmethod of CPSs utilizing stochastic optimization. In state-of-the-artfalsification methods, the result of the previous falsification trials isdiscarded, and we always try to falsify without any prior knowledge. Toconcisely memorize such prior information on the CPS model and exploit it, weemploy Black-box checking (BBC), which is a combination of automata learningand model checking. Moreover, we enhance BBC using the robust semantics of STLformulas, which is the essential gadget in falsification. Our experimentresults suggest that our robustness-guided BBC outperforms a state-of-the-artfalsification tool.

Falsification of Cyber-Physical Systems with Robustness-Guided Black-Box Checking