Falsification of hybrid systems is attracting ever-growing attention in quality assurance of Cyber-Physical Systems (CPS) as a practical alternative to exhaustive formal verification. In falsification, one searches for a falsifying input that drives a given black-box model to output an undesired signal. In this paper, we identify input constraints—such as the constraint “the throttle and brake pedals should not be pressed simultaneously” for an automotive powertrain model—as a key factor for the practical value of falsification methods. We propose three approaches for systematically addressing input constraints in optimization-based falsification, two among which come from the lexicographic method studied in the context of constrained multi-objective optimization. Our experiments show the approaches’ effectiveness.
CITATION STYLE
Zhang, Z., Arcaini, P., & Hasuo, I. (2020). Constraining Counterexamples in Hybrid System Falsification: Penalty-Based Approaches. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12229 LNCS, pp. 401–419). Springer. https://doi.org/10.1007/978-3-030-55754-6_24
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