{"id":358,"date":"2018-11-06T00:57:02","date_gmt":"2018-11-06T00:57:02","guid":{"rendered":"http:\/\/se.kaist.ac.kr\/starlab\/?page_id=358"},"modified":"2018-12-10T03:17:37","modified_gmt":"2018-12-10T03:17:37","slug":"statistical-model-checking","status":"publish","type":"page","link":"https:\/\/se.kaist.ac.kr\/starlab\/studies\/study-2-statistical-verification-of-sos\/statistical-model-checking\/","title":{"rendered":"Statistical Model Checking"},"content":{"rendered":"
Since an SoS is composed of many autonomous and independent CSs, SoS behavior is uncertain and unpredictable. Modeling an SoS with a probabilistic model is considered the proper way to deal with this issue. Probabilistic models express various behaviors using probability. When a probabilistic model comes to the verification stage, Statistical Model Checking (SMC) is a promising method of verification, especially when the model is large and\/or complex. SMC is based on hypothesis testing. It checks the verification property of a sample set of simulations to decide whether the model satisfies the property within a certain probability, which is based on the number of executions for which the property holds compared to the total number of executions in the sample set. Such sampling-based methods are far less memory and time intensive than traditional model checking methods. In this paper, we use an SMC tool called PRISM to model and verify an SoS that exhibit probabilistic behavior.<\/p>\n
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We verify the SoS-level goal achievement by claiming it as the verification property. We perform SMC using PRISM models. We first explain the verification property (i.e., the SoS-level goal) for the MCI example. We provide the verification results.<\/p>\n