Abstract
Hybrid system falsification employs stochastic optimization to search for counterexamples to a system specification in Signal Temporal Logic (STL), guided by quantitative STL robustness. The scale problem could arise when the STL formula is composed of sub-formulas concerning signals having different scales (e.g., speed [km/h] and rpm): the performance of falsification could be negatively affected because different scales can mask each other’s contribution to robustness. A natural solution consists in rescaling the signals to the same order of magnitude. In this paper, we investigate whether this “basic” approach is always effective, or better rescaling strategies could be devised. Experimental results show that basic rescaling is not always the best strategy, and sometimes “unbalanced” rescalings work better. We investigate the reasons of this, and we identify future research directions based on this observation.
This work is supported in part by JSPS KAKENHI Grant No. 20H04168, 19K24348, 19H04086, and JST-Mirai Program Grant No. JPMJMI18BB, Japan. Paolo Arcaini and Ichiro Hasuo are supported by ERATO HASUO Metamathematics for Systems Design Project (No. JPMJER1603), JST.
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Notes
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The source code is available at https://github.com/choshina/FalSTAR-NFM.
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Zhang, Z., Lyu, D., Arcaini, P., Ma, L., Hasuo, I., Zhao, J. (2021). On the Effectiveness of Signal Rescaling in Hybrid System Falsification. In: Dutle, A., Moscato, M.M., Titolo, L., Muñoz, C.A., Perez, I. (eds) NASA Formal Methods. NFM 2021. Lecture Notes in Computer Science(), vol 12673. Springer, Cham. https://doi.org/10.1007/978-3-030-76384-8_24
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