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Faster Statistical Model Checking by Means of Abstraction and Learning

  • Conference paper
Runtime Verification (RV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 8734))

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Abstract

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.

Research supported by the European Community’s Seventh Framework Programme [FP7] under grant agreements No. 257414 (ASCENS), No. 288175 (CERTAINTY), and the French BGLE project ACOSE.

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Author information

Authors and Affiliations

  1. Univ. Grenoble Alpes, VERIMAG, F-38000, Grenoble, France

    Ayoub Nouri, Balaji Raman, Marius Bozga & Saddek Bensalem

  2. CNRS, VERIMAG, F-38000, Grenoble, France

    Ayoub Nouri, Balaji Raman, Marius Bozga & Saddek Bensalem

  3. INRIA/IRISA, Rennes, France

    Axel Legay

Authors
  1. Ayoub Nouri
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  2. Balaji Raman
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  3. Marius Bozga
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  4. Axel Legay
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  5. Saddek Bensalem
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Editor information

Editors and Affiliations

  1. Department of Computing and Software, McMaster University, 1280 Main Street West, L8S 4L7, Hamilton, ON, Canada

    Borzoo Bonakdarpour

  2. Department of Computer Science, State University of New York at Stony Brook, 1423 Computer Science, 11794-4400, Stony Brook, NY, USA

    Scott A. Smolka

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Nouri, A., Raman, B., Bozga, M., Legay, A., Bensalem, S. (2014). Faster Statistical Model Checking by Means of Abstraction and Learning. In: Bonakdarpour, B., Smolka, S.A. (eds) Runtime Verification. RV 2014. Lecture Notes in Computer Science, vol 8734. Springer, Cham. https://doi.org/10.1007/978-3-319-11164-3_28

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  • DOI: https://doi.org/10.1007/978-3-319-11164-3_28

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11163-6

  • Online ISBN: 978-3-319-11164-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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