On-the-Fly Confluence Detection for Statistical Model Checking

  • Arnd Hartmanns
  • Mark Timmer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7871)


Statistical model checking is an analysis method that circumvents the state space explosion problem in model-based verification by combining probabilistic simulation with statistical methods that provide clear error bounds. As a simulation-based technique, it can only provide sound results if the underlying model is a stochastic process. In verification, however, models are usually variations of nondeterministic transition systems. The notion of confluence allows the reduction of such transition systems in classical model checking by removing spurious nondeterministic choices. In this paper, we show that confluence can be adapted to detect and discard such choices on-the-fly during simulation, thus extending the applicability of statistical model checking to a subclass of Markov decision processes. In contrast to previous approaches that use partial order reduction, the confluence-based technique can handle additional kinds of nondeterminism. In particular, it is not restricted to interleavings. We evaluate our approach, which is implemented as part of the modes simulator for the Modest modelling language, on a set of examples that highlight its strengths and limitations and show the improvements compared to the partial order-based method.


Model Check Markov Decision Process Atomic Proposition Reduction Function Outgoing Transition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Arnd Hartmanns
    • 1
  • Mark Timmer
    • 2
  1. 1.Computer ScienceSaarland UniversitySaarbrückenGermany
  2. 2.Formal Methods and ToolsUniversity of TwenteThe Netherlands

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