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.

This paper presents a new state-space generation approach for dynamic fault trees (DFTs) that exploits several successful reduction techniques from the field of model checking. The key idea is to aggressively exploit the DFT structure-detecting symmetries, spurious nondeterminism, and don't cares. Benchmarks show a gain of more than two orders of magnitude in terms of state-space generation and analysis time. This fast, scalable approach is complemented by an approximative technique that determines bounds on DFT measures by a partial state-space generation. This is shown to yield another order of magnitude gain while guaranteeing tight error bounds.