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Extending Automata Learning to Extended Finite State Machines

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Machine Learning for Dynamic Software Analysis: Potentials and Limits

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11026))

Abstract

Automata learning is an established class of techniques for inferring automata models by observing how they respond to a sample of input words. Recently, approaches have been presented that extend these techniques to infer extended finite state machines (EFSMs) by dynamic black-box analysis. EFSMs model both data flow and control behavior, and their mutual interaction. Different dialects of EFSMs are widely used in tools for model-based software development, verification, and testing.

This survey paper presents general principles behind some of these recent extensions. The goal is to elucidate how the principles behind classic automata learning can be maintained and guide extensions to more general automata models, and to situate some extensions with respect to these principles.

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Notes

  1. 1.

    The Nerode equivalence defined in [CHJ+15b] is defined only for the theory of equalities over an infinite domain, and can be obtained as a special case of the approach described in Sect. 6.

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Acknowledgment

This work was supported in part by the European FP7 project CONNECT (IST 231167), and by the Swedish Research Council as part of the UPMARC centre of excellence.

Author information

Authors and Affiliations

  1. Scania CV AB, Södertälje, Sweden

    Sofia Cassel

  2. Dortmund University of Technology and Fraunhofer ISST, Dortmund, Germany

    Falk Howar

  3. Department of Information Technology, Uppsala University, Uppsala, Sweden

    Bengt Jonsson

  4. Chair for Programming Systems, TU Dortmund, Dortmund, Germany

    Bernhard Steffen

Authors
  1. Sofia Cassel
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  2. Falk Howar
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  3. Bengt Jonsson
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  4. Bernhard Steffen
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Corresponding author

Correspondence to Bengt Jonsson .

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Editors and Affiliations

  1. The Open University, Milton Keynes, United Kingdom

    Amel Bennaceur

  2. Technische Universität Darmstadt, Darmstadt, Germany

    Reiner Hähnle

  3. KTH Royal Institute of Technology, Stockholm, Sweden

    Karl Meinke

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Cassel, S., Howar, F., Jonsson, B., Steffen, B. (2018). Extending Automata Learning to Extended Finite State Machines. In: Bennaceur, A., Hähnle, R., Meinke, K. (eds) Machine Learning for Dynamic Software Analysis: Potentials and Limits. Lecture Notes in Computer Science(), vol 11026. Springer, Cham. https://doi.org/10.1007/978-3-319-96562-8_6

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  • DOI: https://doi.org/10.1007/978-3-319-96562-8_6

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