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IFIP International Conference on Testing Software and Systems

ICTSS 2020: Testing Software and Systems pp 52–69Cite as

Learning Abstracted Non-deterministic Finite State Machines

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12543))

Abstract

Active automata learning gains increasing interest since it gives an insight into the behavior of a black-box system. A crucial drawback of the frequently used learning algorithms based on Angluin’s \(L^*\) is that they become impractical if systems with a large input/output alphabet are learned. Previous work suggested to circumvent this problem by abstracting the input alphabet and the observed outputs. However, abstraction could introduce non-deterministic behavior. Already existing active automata learning algorithms for observable non-deterministic systems learn larger models if outputs are only observable after certain input/output sequences. In this paper, we introduce an abstraction scheme that merges akin states. Hence, we learn a more generic behavioral model of a black-box system. Furthermore, we evaluate our algorithm in a practical case study. In this case study, we learn the behavior of five different Message Queuing Telemetry Transport (mqtt) brokers interacting with multiple clients.

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Acknowledgments

This work is supported by the TU Graz LEAD project “Dependable Internet of Things in Adverse Environments”. We would like to thank student Jorrit Stramer for the implementation of the MQTT client.

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Authors and Affiliations

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

    Andrea Pferscher & Bernhard K. Aichernig

Authors
  1. Andrea Pferscher
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  2. Bernhard K. Aichernig
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Correspondence to Andrea Pferscher .

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Editors and Affiliations

  1. University of Naples Federico II, Naples, Italy

    Valentina Casola

  2. University of Naples Federico II, Naples, Italy

    Alessandra De Benedictis

  3. University of Campania "Luigi Vanvitelli", Aversa, Italy

    Massimiliano Rak

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Pferscher, A., Aichernig, B.K. (2020). Learning Abstracted Non-deterministic Finite State Machines. In: Casola, V., De Benedictis, A., Rak, M. (eds) Testing Software and Systems. ICTSS 2020. Lecture Notes in Computer Science(), vol 12543. Springer, Cham. https://doi.org/10.1007/978-3-030-64881-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-64881-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64880-0

  • Online ISBN: 978-3-030-64881-7

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