Stochastic model checking is a technique for analyzing systems that possess probabilistic characteristics. However, its scalability is limited as probabilistic models of real-world applications typically have very large or infinite state space. This paper presents a new infinite state CTMC model checker, STAMINA, with improved scalability. It uses a novel state space approximation method to reduce large and possibly infinite state CTMC models to finite state representations that are amenable to existing stochastic model checkers. It is integrated with a new property-guided state expansion approach that improves the analysis accuracy. Demonstration of the tool on several benchmark examples shows promising results in terms of analysis efficiency and accuracy compared with a state-of-the-art CTMC model checker that deploys a similar approximation method.
STAMINA: STochastic Approximate Model-Checker for INfinite-State AnalysisLecture Notes in Computer Science
Document TypeConference Paper
LocationNew York City, New York
Creative Commons LicenseCreative Commons Attribution 4.0
Citation InformationNeupane T., Myers C.J., Madsen C., Zheng H., Zhang Z. (2019) STAMINA: STochastic Approximate Model-Checker for INfinite-State Analysis. In: Dillig I., Tasiran S. (eds) Computer Aided Verification. CAV 2019. Lecture Notes in Computer Science, vol 11561. Springer, Cham