文章基本信息

标题：Monte Carlo Based Statistical Model Checking of Cyber-Physical Systems: A Review
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作者：Angela Pappagallo ; Annalisa Massini ; Enrico Tronci 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2020
卷号：11
期号：12
页码：1-24
DOI：10.3390/info11120588
出版社：MDPI Publishing
摘要：The ever-increasing deployment of autonomous Cyber-Physical Systems (CPSs) (e.g., autonomous cars, UAV) exacerbates the need for efficient formal verification methods. In this setting, the main obstacle to overcome is the huge number of scenarios to be evaluated. Statistical Model Checking (SMC) is a simulation-based approach that holds the promise to overcome such an obstacle by using statistical methods in order to sample the set of scenarios. Many SMC tools exist, and they have been reviewed in several works. In this paper, we will overview Monte Carlo-based SMC tools in order to provide selection criteria based on Key Performance Indicators (KPIs) for the verification activity (e.g., minimize verification time or cost) as well as on the environment features, the kind of system model, the language used to define the requirements to be verified, the statistical inference approach used, and the algorithm implementing it. Furthermore, we will identify open research challenges in the field of (SMC) tools.
关键词：statistical model checking; Cyber-Physical Systems; Monte Carlo sampling statistical model checking ; Cyber-Physical Systems ; Monte Carlo sampling