A Theory of History Dependent Abstractions for Learning Interface Automata

  • Fides Aarts
  • Faranak Heidarian
  • Frits Vaandrager
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7454)


History dependent abstraction operators are the key for scaling existing methods for active learning of automata to realistic applications. Recently, Aarts, Jonsson & Uijen have proposed a framework for history dependent abstraction operators. Using this framework they succeeded to automatically infer models of several realistic software components with large state spaces, including fragments of the TCP and SIP protocols. Despite this success, the approach of Aarts et al. suffers from limitations that seriously hinder its applicability in practice. In this article, we get rid of some of these limitations and present four important generalizations/improvements of the theory of history dependent abstraction operators. Our abstraction framework supports: (a) interface automata instead of the more restricted Mealy machines, (b) the concept of a learning purpose, which allows one to restrict the learning process to relevant behaviors only, (c) a richer class of abstractions, which includes abstractions that overapproximate the behavior of the system-under-test, and (d) a conceptually superior approach for testing correctness of the hypotheses that are generated by the learner.


Output Action Reachable State Label Transition System Process Algebra System Under Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Fides Aarts
    • 1
  • Faranak Heidarian
    • 1
  • Frits Vaandrager
    • 1
  1. 1.Institute for Computing and Information SciencesRadboud University NijmegenNijmegenThe Netherlands

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