Faster Statistical Model Checking by Means of Abstraction and Learning

  • Ayoub Nouri
  • Balaji Raman
  • Marius Bozga
  • Axel Legay
  • Saddek Bensalem
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8734)


This paper investigates the combined use of abstraction and probabilistic learning as a means to enhance statistical model checking performance. We are given a property (or a list of properties) for verification on a (large) stochastic system. We project on a set of traces generated from the original system, and learn a (small) abstract model from the projected traces, which contain only those labels that are relevant to the property to be verified. Then, we model-check the property on the reduced, abstract model instead of the large, original system. In this paper, we propose a formal definition of the projection on traces given a property to verify. We also provide conditions ensuring the correct preservation of the property on the abstract model. We validate our approach on the Herman’s Self Stabilizing protocol. Our experimental results show that (a) the size of the abstract model and the verification time are drastically reduced, and that (b) the probability of satisfaction of the property being verified is correctly estimated by statistical model checking on the abstract model with respect to the concrete system.


Model Check Abstract Model Atomic Proposition Sequential Probability Ratio Test Linear Time Temporal Logic 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Ayoub Nouri
    • 1
    • 2
  • Balaji Raman
    • 1
    • 2
  • Marius Bozga
    • 1
    • 2
  • Axel Legay
    • 3
  • Saddek Bensalem
    • 1
    • 2
  1. 1.Univ. Grenoble Alpes, VERIMAGGrenobleFrance
  2. 2.CNRS, VERIMAGGrenobleFrance
  3. 3.INRIA/IRISARennesFrance

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