On the Effectiveness of Signal Rescaling in Hybrid System Falsification

Zhenya Zhang, Deyun Lyu, Paolo Arcaini, Lei Ma, Ichiro Hasuo, Jianjun Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationNASA Formal Methods - 13th International Symposium, NFM 2021, Proceedings
EditorsAaron Dutle, César A. Muñoz, Mariano M. Moscato, Laura Titolo, Ivan Perez
PublisherSpringer Science and Business Media Deutschland GmbH
Pages392-399
Number of pages8
ISBN (Print)9783030763831
DOIs
Publication statusPublished - 2021
Event13th International Symposium on NASA Formal Methods, NFM 2021 - Virtual, Online
Duration: May 24 2021May 28 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12673 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Symposium on NASA Formal Methods, NFM 2021
CityVirtual, Online
Period5/24/215/28/21

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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