A Storm is Coming: A Modern Probabilistic Model Checker

  title={A Storm is Coming: A Modern Probabilistic Model Checker},
  author={Christian Dehnert and Sebastian Junges and Joost-Pieter Katoen and Matthias Volk},
We launch the new probabilistic model checker Storm. [] Key Method 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.

The Probabilistic Model Checker Storm

The main features of Storm are reported and how to effectively use them are explained and an empirical evaluation of different configurations of Storm on the QComp 2019 benchmark set is presented.

An efficient statistical model checker for nondeterminism and rare events

Statistical model checking avoids the state space explosion problem in verification and naturally supports complex non-Markovian formalisms. Yet as a simulation-based approach, its runtime becomes

Probabilistic Program Verification via Inductive Synthesis of Inductive Invariants

An inductive synthesis approach for proving quantitative reachability properties by proving inductive invariants on source-code level by beating state-of-the-art model checkers on some benchmarks and often outperforming monolithic alternatives.

FIG: The Finite Improbability Generator

This paper introduces the statistical model checker FIGV, that estimates transient and steady-state reachability properties in stochastic automata, and can outperform other state-of-the-art tools for Rare Event Simulation.

Bayesian Inference by Symbolic Model Checking

A simple translation from Bayesian networks into tree-like Markov chains such that inference can be reduced to computing reachability probabilities using probabilistic sentential decision diagrams and vtrees, a scalable symbolic technique in AI inference tools.

Model-based testing of stochastically timed systems

This paper presents two model-based testing frameworks that additionally cover the stochastic aspects in hard and soft real-time systems and highlights the trade-off of simple and efficient statistical evaluation for Markov automata versus precise and realistic modelling with Stochastic automata.

A Modest Approach to Modelling and Checking Markov Automata

Extensions to the Modest language and the mcsta model checker are presented to describe and analyse Markov automata models and it is shown that mcsta improves the performance and scalability of Markov Automata model checking compared to earlier and alternative tools.

Inductive Synthesis for Probabilistic Programs Reaches New Horizons

A novel inductive oracle that greedily generates counter-examples for violating programs and uses them to prune the family to provide a significantly faster and more effective pruning strategy leading to an accelerated synthesis process on a wide range of benchmarks.

Computing Conditional Probabilities: Implementation and Evaluation

This paper reports on the main features of an implementation of computation schemes for conditional probabilities in discrete-time Markov chains and Markov decision processes within the probabilistic model checker Prism and a comparative experimental evaluation.

Sound Value Iteration

This work presents an alternative that does not require the a priori computation of starting vectors and that converges faster on many benchmarks and gives tight and safe bounds - whose computation is cheap - on the reachability probabilities.



iscasMc: A Web-Based Probabilistic Model Checker

This paper introduces the web-based model checker iscasMc, an easy-to-use web interface for the evaluation of Markov chains and decision processes against P CTL and PCTL* specifications that is particularly efficient in evaluating the probabilities of LTL properties.

The Probabilistic Model Checking Landscape*

  • J. Katoen
  • Computer Science
    2016 31st Annual ACM/IEEE Symposium on Logic in Computer Science (LICS)
  • 2016
This paper surveys the algorithmic verification of probabilistic models, in particular Probabilistic model checking, and provides an informal account of the main models, the underlying algorithms, applications from reliability and dependability analysis—and beyond—and describes recent developments towards automated parameter synthesis.

DiPro - A Tool for Probabilistic Counterexample Generation

An open source tool called DiPro is presented that can be used with the PRISM and MRMC probabilistic model checkers and allows for the computation of Probabilistic counterexamples for discrete time Markov chains, continuous timeMarkov chains and Markov decision processes.

The Ins and Outs of the Probabilistic Model Checker MRMC

Symbolic and Parametric Model Checking of Discrete-Time Markov Chains

A language-theoretic approach to symbolic model checking of PCTL over discrete-time Markov chains, which allows for parametric model checking by evaluating the regular expression for different parameter values, to study the influence of a lossy channel in the overall reliability of a randomized protocol.

(Stochastic) Model Checking in GreatSPN

Through a new (Java-based) graphical interface for the GSPN model definition, the user can now access model checking of three different logics: the classical branching temporal logic CTL, and two stochastic logics, CSL and its superset CSLTA.

MARCIE - Model Checking and Reachability Analysis Done EffiCIEntly

MARCIE's architecture and its most important distinguishing features are presented, and extensive computational experiments demonstrate MARCIE's strength in comparison with related tools.

On Probabilistic Automata in Continuous Time

We develop a compositional behavioural model that integrates a variation of probabilistic automata into a conservative extension of interactive Markov chains. The model is rich enough to embody the

Counterexample Generation for Discrete-Time Markov Models: An Introductory Survey

This paper is an introductory survey of available methods for the computation and representation of probabilistic counterexamples for discrete-time Markov chains and Probabilistic automata, using explicit and symbolic techniques.

PRISM 4.0: Verification of Probabilistic Real-Time Systems

A major new release of the PRISMprobabilistic model checker is described, adding, in particular, quantitative verification of (priced) probabilistic timed automata.