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.

Quantitative formal models capture probabilistic behaviour, real-time aspects, or general continuous dynamics. A number of tools support their automatic analysis with respect to dependability or performance properties. QComp 2019 is the first, friendly competition among such tools. It focuses on stochastic formalisms from Markov chains to probabilistic timed automata specified in the Jani model exchange format, and on probabilistic reachability, expected-reward, and steady-state properties. QComp draws its benchmarks from the new Quantitative Verification Benchmark Set. Participating tools, which include probabilistic model checkers and planners as well as simulation-based tools, are evaluated in terms of performance, versatility, and usability. In this paper, we report on the challenges in setting up a quantitative verification competition, present the results of QComp 2019, summarise the lessons learned, and provide an outlook on the features of the next edition of QComp.

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Markov chain analysis is a key technique in reliability engineering. A practical obstacle is that all probabilities in Markov models need to be known. However, system quantities such as failure rates or packet loss ratios, etc. are often notor only partially-known. This motivates considering parametric models with transitions labeled with functions over parameters.Whereas traditional Markov chain analysis evaluates a reliability metric for a single, fixed set of probabilities, analysing parametric Markov models focuses on synthesising parameter values that establish a given reliability or performance specification ϕ. Examples are: what component failure rates ensure the probability of a system breakdown to be below 0.00000001?, or which failure rates maximise reliability?This paper presents various analysis algorithms for parametric Markov chains and Markov decision processes. We focus on three problems: (a) do all parameter values within a given region satisfy ϕ?, (b) which regions satisfy ϕ and which ones do not?, and (c) an approximate version of (b) focusing on covering a large fraction of all possible parameter values.We give a detailed account of the various algorithms, present a software tool realising these techniques, and report on an extensive experimental evaluation on benchmarks that span a wide range of applications.