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Falsification, given a desired temporal specification, tries to find an input of violation instead of a proof guarantee. The state-of-the-art falsification approaches often employ stochastic hill-climbing optimization that minimizes the degree of satisfaction of the temporal specification, given by its quantitative<jats:italic>robust semantics<\/jats:italic>. However, it has been shown that the performance of falsification could be severely affected by the so-called<jats:italic>scale problem<\/jats:italic>, related to the different scales of the signals used in the specification (e.g., rpm and speed): in the robustness computation, the contribution of a signal could be<jats:italic>masked<\/jats:italic>by another one. In this paper, we propose a novel approach to tackle this problem. We first introduce a new robustness definition, called<jats:italic>QB-Robustness<\/jats:italic>, which combines classical Boolean satisfaction and quantitative robustness. We prove that QB-Robustness can be used to judge the satisfaction of the specification and avoid the scale problem in its computation. QB-Robustness is exploited by a falsification approach based on Monte Carlo Tree Search over the structure of the formal specification. First, tree traversal identifies the sub-formulas for which it is needed to compute the quantitative robustness. Then, on the leaves, numerical hill-climbing optimization is performed, aiming to falsify such sub-formulas. Our in-depth evaluation on multiple benchmarks demonstrates that our approach achieves better falsification results than the state-of-the-art falsification approaches guided by the classical quantitative robustness, and it is largely not affected by the scale problem.<\/jats:p>","DOI":"10.1007\/978-3-030-81685-8_29","type":"book-chapter","created":{"date-parts":[[2021,7,17]],"date-time":"2021-07-17T00:02:35Z","timestamp":1626480155000},"page":"595-618","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Effective Hybrid System Falsification Using Monte Carlo Tree Search Guided by QB-Robustness"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3854-9846","authenticated-orcid":false,"given":"Zhenya","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3017-7977","authenticated-orcid":false,"given":"Deyun","family":"Lyu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6253-4062","authenticated-orcid":false,"given":"Paolo","family":"Arcaini","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8621-2420","authenticated-orcid":false,"given":"Lei","family":"Ma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8300-4650","authenticated-orcid":false,"given":"Ichiro","family":"Hasuo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8083-4352","authenticated-orcid":false,"given":"Jianjun","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,15]]},"reference":[{"key":"29_CR1","doi-asserted-by":"crossref","unstructured":"Abbas, H., Fainekos, G.: Convergence proofs for simulated annealing falsification of safety properties. 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