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Timed Automata Learning via SMT Solving

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NASA Formal Methods (NFM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13260))

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Abstract

Automata learning is a technique for automatically inferring models of existing systems, that enables formal verification of black-box systems. In this paper we propose a way of learning timed automata, extended final state machines that can measure the progress of time. We make use of SMT solving to learn timed automata consistent with the observations in a set of timed traces, which can be gathered via active testing or passive monitoring. By imposing a set of restrictions to the learnt models, we ensure that our solutions are not overly general. The presented SMT encoding of the problem allows for two ways of incremental solving and different search orders. We present a prototype implementation with results from case studies and randomly generated timed automata of varying size and complexity. We perform an extensive evaluation over six SMT solvers, using different theories and exploration strategies, as well as incremental and non-incremental solving.

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Notes

  1. 1.

    Issue # 1459 on GitHub (https://github.com/Z3Prover/z3/issues/1459 discusses a similar case involving the floating point theory.

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Acknowledgments

This work has been supported by the “University SAL Labs” initiative of Silicon Austria Labs (SAL) and its Austrian partner universities for applied fundamental research for electronic based systems.

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Authors and Affiliations

  1. Institute of Software Technology, Graz University of Technology, Graz, Austria

    Martin Tappler & Bernhard K. Aichernig

  2. Silicon Austria Labs, TU Graz – SAL DES Lab, Graz, Austria

    Martin Tappler

  3. Aalborg University, Aalborg, Denmark

    Florian Lorber

Authors
  1. Martin Tappler
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  2. Bernhard K. Aichernig
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  3. Florian Lorber
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Correspondence to Martin Tappler .

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Editors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    Jyotirmoy V. Deshmukh

  2. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Klaus Havelund

  3. National Institute of Aerospace, Hampton, VA, USA

    Ivan Perez

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Tappler, M., Aichernig, B.K., Lorber, F. (2022). Timed Automata Learning via SMT Solving. In: Deshmukh, J.V., Havelund, K., Perez, I. (eds) NASA Formal Methods. NFM 2022. Lecture Notes in Computer Science, vol 13260. Springer, Cham. https://doi.org/10.1007/978-3-031-06773-0_26

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  • DOI: https://doi.org/10.1007/978-3-031-06773-0_26

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