2021
DOI: 10.1007/s10009-021-00633-z
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The probabilistic model checker Storm

Abstract: 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. … Show more

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Cited by 92 publications
(77 citation statements)
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“…Implementation details. We integrated Algorithm 1 in the probabilistic model checker Storm [22] as an extension of the POMDP verification framework described in [8]. Inputs are a POMDP-encoded either explicitly or using an extension of the Prism language [36]-and a property specification.…”
Section: Experimental Evaluationmentioning
confidence: 99%
See 1 more Smart Citation
“…Implementation details. We integrated Algorithm 1 in the probabilistic model checker Storm [22] as an extension of the POMDP verification framework described in [8]. Inputs are a POMDP-encoded either explicitly or using an extension of the Prism language [36]-and a property specification.…”
Section: Experimental Evaluationmentioning
confidence: 99%
“…Experimental results. We have implemented our cut-off and belief clipping approaches on top of the probabilistic model checker Storm [22] and applied it to a range of various benchmarks. We provide a comparison with the model checking approach in [36], and determine the tightness of our under-approximations by comparing them to over-approximations obtained using the algorithm from [8].…”
Section: Introductionmentioning
confidence: 99%
“…It is written in Python and is well integrated with interactive Jupyter notebooks [19] for visualization of CMDPs and algorithms. With STORM [1] and STORMPY installed, FIMDP can read models in PRISM [20] or JANI [21] languages.…”
Section: A Tools Examples and Evaluation Settingmentioning
confidence: 99%
“…In particular, the goal-leaning and threshold heuristics attempt, as a secondary objective, to reach T in a short time. Further, we briefly describe our tool implementing these algorithms and we demonstrate that our approach specialized to qualitative analysis of resource-constrained systems can solve this task faster then the state-of-the-art generalpurpose probabilistic model checker STORM [1].…”
Section: Introductionmentioning
confidence: 99%
“…Implementation. We implemented the BDD translation for static fault trees (SFTs) in the Storm model checker [31] and use the multi-core BDD library Sylvan [19]. Our implementation Storm-dft supports computing minimal cut sets (MCS), the unreliability and several importance measures such as the Birnbaum index [7].…”
Section: Introductionmentioning
confidence: 99%