Lecture Notes in Computer Science
New York City, New York
NSF, Division of Undergraduate Education (DUE) 1856733
NSF, Division of Undergraduate Education (DUE)
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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.
Neupane, Thakur, et al. “STAMINA: STochastic Approximate Model-Checker for INfinite-State Analysis.” Computer Aided Verification, edited by Isil Dillig and Serdar Tasiran, vol. 11561, Springer International Publishing, 2019, pp. 540–49. DOI.org (Crossref), doi:10.1007/978-3-030-25540-4_31.