Significant Diagnostic Counterexamples in Probabilistic Model Checking

  • Miguel E. Andrés
  • Pedro D’Argenio
  • Peter van Rossum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5394)


This paper presents a novel technique for counterexample generation in probabilistic model checking of Markov chains and Markov Decision Processes. (Finite) paths in counterexamples are grouped together in witnesses that are likely to provide similar debugging information to the user. We list five properties that witnesses should satisfy in order to be useful as debugging aid: similarity, accuracy, originality, significance, and finiteness. Our witnesses contain paths that behave similarly outside strongly connected components.

Then, we show how to compute these witnesses by reducing the problem of generating counterexamples for general properties over Markov Decision Processes, in several steps, to the easy problem of generating counterexamples for reachability properties over acyclic Markov chains.


Markov Chain Model Check Markov Decision Process Linear Temporal Logic Propositional Formula 
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 2009

Authors and Affiliations

  • Miguel E. Andrés
    • 1
  • Pedro D’Argenio
    • 2
  • Peter van Rossum
    • 1
  1. 1.Institute for Computing and Information SciencesThe Netherlands
  2. 2.FaMAF, Universidad Nacional de Córdoba, CONICETArgentina

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