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Learning of Automata Models Extended with Data

  • Chapter
Formal Methods for Eternal Networked Software Systems (SFM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 6659))

Abstract

One of the challenges in the Connect project is to develop techniques for learning models of networked components from exploratory interaction with the component, based on analyzing messages exchanged between the component and its environment. Many approaches to this problem employ regular inference (aka. automata learning) techniques which generate modest-size finite-state models. Most communication with real-life systems involves data values being relevant to the communication context and thus influencing the observable behavior of the communication endpoints. When applying methods from the realm of automata learning, it is desirable to handle such data-occurrences. It is therefore important to extend inference techniques to handle message alphabets and state-spaces with structures containing data parameters, often with large domains. After very briefly mentioning several approaches to the problem, we give a longer account of an approach proposed by Aarts et al, which adapts ideas from of predicate abstraction, successfully used in formal verification. We illustrate the techniques by application to a simple running example, which models a simple booking service.

Supported in part by EC Proj. 231167 (CONNECT).

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Author information

Authors and Affiliations

  1. Department of Computer Systems, Uppsala University, Sweden

    Bengt Jonsson

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  1. Bengt Jonsson
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Editors and Affiliations

  1. Dipartimento di Scienze di Base e Fondamenti, Università di Urbino “Carlo Bo”, Piazza della Repubblica 13, 61029, Urbino, Italy

    Marco Bernardo

  2. INRIA Paris-Rocquencourt, France

    Valérie Issarny

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Jonsson, B. (2011). Learning of Automata Models Extended with Data. In: Bernardo, M., Issarny, V. (eds) Formal Methods for Eternal Networked Software Systems. SFM 2011. Lecture Notes in Computer Science, vol 6659. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21455-4_10

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  • DOI: https://doi.org/10.1007/978-3-642-21455-4_10

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  • Print ISBN: 978-3-642-21454-7

  • Online ISBN: 978-3-642-21455-4

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