scholarly journals Hybrid Statistical Model Checking Technique for Reliable Safety Critical Systems

2012 ◽  
Author(s):  
Youngjoo Kim ◽  
Moonzoo Kim
Keyword(s):  
Model Checking ◽  
Statistical Model ◽  
Critical Systems ◽  
Statistical Model Checking ◽  
Safety Critical ◽  
Safety Critical Systems

scholarly journals Model Checking Safety-Critical Systems Using Safecharts

2007 ◽  
Vol 56 (5) ◽  
pp. 692-705 ◽  
Author(s):  
Pao-Ann Hsiung ◽  
Yean-Ru Chen ◽  
Yen-Hung Lin
Keyword(s):  
Model Checking ◽  
Critical Systems ◽  
Safety Critical ◽  
Safety Critical Systems

scholarly journals Towards a Statistical Model Checking Method for Safety-Critical Cyber-Physical System Verification

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jian Xie ◽  
Wenan Tan ◽  
Bingwu Fang ◽  
Zhiqiu Huang
Keyword(s):  
Model Checking ◽  
Statistical Model ◽  
Importance Sampling ◽  
Physical System ◽  
Estimation Method ◽  
Environmental Damage ◽  
Cyber Physical System ◽  
Statistical Model Checking ◽  
Safety Critical ◽  
System Verification

Safety-Critical Cyber-Physical System (SCCPS) refers to the system that if the system fails or its key functions fail, it will cause casualties, property damage, environmental damage, and other catastrophic consequences. Therefore, it is vital to verify the safety of safety critical systems. In the community, the SCCPS safety verification mainly relies on the statistical model checking methodology, but for SCCPS with extremely high safety requirements, the statistical model checking method is difficult/infeasible to sample the extremely small probability event since the probability of the system violating the safety is very low (rare property). In response to this problem, we propose a new method of statistical model checking for high-safety SCCPS. Firstly, with the CTMC-approximated SCCPS path probability space model, it leverages the maximum likelihood estimation method to learn the parameters of CTMC. Then, the embedded DTMC can be derived from CTMC, and a cross-entropy optimization model based on DTMC can be constructed. Finally, we propose an algorithm of iteratively learning the optimal importance sampling distribution on the discrete path space and an algorithm to check the statistical model of verifying the rare attribute. Eventually, experimental results show that the method proposed in this paper can effectively verify the rare attributes of SCCPS. Under the same sample size, comparing with the heuristic importance sampling methods, the estimated value of this method can be better distributed around the mean value, and the related standard deviation and relative error are reduced by more than an order of magnitude.

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