Document Type
Conference Paper
Journal/Book Title/Conference
Lecture Notes in Computer Science
Volume
11561
Publisher
Springer
Location
New York City, New York
Publication Date
7-12-2019
Award Number
NSF, Division of Undergraduate Education (DUE) 1856733
Funder
NSF, Division of Undergraduate Education (DUE)
First Page
540
Last Page
549
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Abstract
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.
Recommended Citation
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.
