We launch the new probabilistic model checker Storm. It features the analysis of discrete-and continuous-time variants of both Markov chains and MDPs. It supports the Prism and JANI modeling languages, probabilistic programs, dynamic fault trees and generalized stochastic Petri nets. It has a modular set-up in which solvers and symbolic engines can easily be exchanged. It offers a Python API for rapid prototyping by encapsulating Storm's fast and scalable algorithms. Experiments on a variety of benchmarks show its competitive performance.

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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.

We present a novel method for computing reachability probabilities of parametric discrete-time Markov chains whose transition probabilities are fractions of polynomials over a set of parameters. Our algorithm is based on two key ingredients: a graph decomposition into strongly connected subgraphs combined with a novel factorization strategy for polynomials. Experimental evaluations show that these approaches can lead to a speed-up of up to several orders of magnitude in comparison to existing approaches. PreliminariesDefinition 1 (Discrete-time Markov chain). A discrete-time Markov chain (DTMC) is a tuple D = (S, I, P ) with a non-empty finite set S of states, an initial