The probabilistic model checker Storm

We present the probabilistic model checker Storm . Storm supports the analysis of discrete- and continuous-time variants of both Markov chains and Markov decision processes. Storm has three major distinguishing features. It supports multiple input languages for Markov models, including the Jani and...

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Veröffentlicht in:International journal on software tools for technology transfer Jg. 24; H. 4; S. 589 - 610
Hauptverfasser: Hensel, Christian, Junges, Sebastian, Katoen, Joost-Pieter, Quatmann, Tim, Volk, Matthias
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2022
Springer Nature B.V
Schlagworte:
Algorithms
Awards & honors
Command languages
Competitions and Challenges
Computer Science
Empirical analysis
Fault trees
Languages
Markov analysis
Markov chains
Petri nets
Probabilistic models
Probability theory
Rapid prototyping
Software Engineering
Software Engineering/Programming and Operating Systems
Theory of Computation
Markov chain
Model checking
Markov decision process
Verification
Probilistic systems
ISSN:1433-2779, 1433-2787
Online-Zugang:Volltext
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Zusammenfassung:We present the probabilistic model checker Storm . Storm supports the analysis of discrete- and continuous-time variants of both Markov chains and Markov decision processes. Storm has three major distinguishing features. It supports multiple input languages for Markov models, including the Jani and Prism modeling languages, dynamic fault trees, generalized stochastic Petri nets, and the probabilistic guarded command language. It has a modular setup in which solvers and symbolic engines can easily be exchanged. Its Python API allows for rapid prototyping by encapsulating Storm ’s fast and scalable algorithms. This paper reports on the main features of Storm and explains how to effectively use them. A description is provided of the main distinguishing functionalities of Storm . Finally, an empirical evaluation of different configurations of Storm on the QComp 2019 benchmark set is presented.
Bibliographie:ObjectType-Article-1
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ISSN:1433-2779
1433-2787
DOI:10.1007/s10009-021-00633-z