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LearnLib: a framework for extrapolating behavioral models

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Abstract

In this paper, we present the LearnLib, a library of tools for automata learning, which is explicitly designed for the systematic experimental analysis of the profile of available learning algorithms and corresponding optimizations. Its modular structure allows users to configure their own tailored learning scenarios, which exploit specific properties of their envisioned applications. As has been shown earlier, exploiting application-specific structural features enables optimizations that may lead to performance gains of several orders of magnitude, a necessary precondition to make automata learning applicable to realistic scenarios.

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Authors and Affiliations

  1. Chair of Programming Systems, TU Dortmund, Otto-Hahn-Str. 14, 44227, Dortmund, Germany

    Harald Raffelt & Bernhard Steffen

  2. Department of Information Technology, Uppsala University, 751 05, Uppsala, Sweden

    Therese Berg

  3. Chair of Services and Software Engineering, Universität Potsdam, August-Bebel-Str. 89, 14482, Potsdam, Germany

    Tiziana Margaria

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  1. Harald Raffelt
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  2. Bernhard Steffen
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  3. Therese Berg
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  4. Tiziana Margaria
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Correspondence to Bernhard Steffen.

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Raffelt, H., Steffen, B., Berg, T. et al. LearnLib: a framework for extrapolating behavioral models. Int J Softw Tools Technol Transfer 11, 393–407 (2009). https://doi.org/10.1007/s10009-009-0111-8

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