Active Learning of Nondeterministic Systems from an ioco Perspective

  • Michele Volpato
  • Jan Tretmans
Conference paper

DOI: 10.1007/978-3-662-45234-9_16

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8802)
Cite this paper as:
Volpato M., Tretmans J. (2014) Active Learning of Nondeterministic Systems from an ioco Perspective. In: Margaria T., Steffen B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Technologies for Mastering Change. ISoLA 2014. Lecture Notes in Computer Science, vol 8802. Springer, Berlin, Heidelberg

Abstract

Model-based testing allows the creation of test cases from a model of the system under test. Often, such models are difficult to obtain, or even not available. Automata learning helps in inferring the model of a system by observing its behaviour. The model can be employed for many purposes, such as testing other implementations, regression testing, or model checking. We present an algorithm for active learning of nondeterministic, input-enabled, labelled transition systems, based on the well known Angluin’s L ⋆  algorithm. Under some assumptions, for dealing with nondeterminism, input-enabledness and equivalence checking, we prove that the algorithm produces a model whose behaviour is equivalent to the one under learning. We define new properties for the structure used in the algorithm, derived from the semantics of labelled transition systems. Such properties help the learning, by avoiding to query the system under learning when it is not necessary.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Michele Volpato
    • 1
  • Jan Tretmans
    • 1
    • 2
  1. 1.Institute for Computing and Information SciencesRadboud Universiteit NijmegenNijmegenThe Netherlands
  2. 2.TNO - Embedded Systems InnovationEindhovenThe Netherlands

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