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Inferring Canonical Register Automata

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Book cover Verification, Model Checking, and Abstract Interpretation (VMCAI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7148))

Abstract

In this paper, we present an extension of active automata learning to register automata, an automaton model which is capable of expressing the influence of data on control flow. Register automata operate on an infinite data domain, whose values can be assigned to registers and compared for equality. Our active learning algorithm is unique in that it directly infers the effect of data values on control flow as part of the learning process. This effect is expressed by means of registers and guarded transitions in the resulting register automata models. The application of our algorithm to a small example indicates the impact of learning register automata models: Not only are the inferred models much more expressive than finite state machines, but the prototype implementation also drastically outperforms the classic L * algorithm, even when exploiting optimal data abstraction and symmetry reduction.

This work is supported by the European FP 7 project CONNECT (IST 231167).

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Author information

Authors and Affiliations

  1. Programming Systems, Technical University Dortmund, Dortmund, D-44227, Germany

    Falk Howar & Bernhard Steffen

  2. Dept. of Information Technology, Uppsala University, Sweden

    Bengt Jonsson & Sofia Cassel

Authors
  1. Falk Howar
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  2. Bernhard Steffen
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  3. Bengt Jonsson
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  4. Sofia Cassel
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Editor information

Editors and Affiliations

  1. Swiss Federal Institute of Technology Lausanne (EPFL), IC IIF LARA INR 318, Station 14, 1015, Lausanne, Switzerland

    Viktor Kuncak

  2. Institut für Informatik, Technische Universität München, Boltzmannstr. 3, 85748, Munich, Germany

    Andrey Rybalchenko

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© 2012 Springer-Verlag Berlin Heidelberg

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Howar, F., Steffen, B., Jonsson, B., Cassel, S. (2012). Inferring Canonical Register Automata. In: Kuncak, V., Rybalchenko, A. (eds) Verification, Model Checking, and Abstract Interpretation. VMCAI 2012. Lecture Notes in Computer Science, vol 7148. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27940-9_17

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  • DOI: https://doi.org/10.1007/978-3-642-27940-9_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27939-3

  • Online ISBN: 978-3-642-27940-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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