The Probabilistic Model Checker Storm
This is an incremental tool development for researchers and practitioners in formal verification and probabilistic modeling.
The authors presented Storm, a probabilistic model checker that supports analysis of various Markov models with multiple input languages and a modular architecture. The paper describes Storm's features and provides an empirical evaluation on the QComp 2019 benchmark set.
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 set-up 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.