LearnLib: a framework for extrapolating behavioral models

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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International Journal on Software Tools for Technology Transfer, Springer Verlag, 2009, 11 (5), pp.393-407. 〈10.1007/s10009-009-0111-8〉
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https://hal.inria.fr/inria-00459959
Contributeur : Brigitte Briot <>
Soumis le : jeudi 25 février 2010 - 16:18:08
Dernière modification le : jeudi 25 février 2010 - 16:18:08

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Harald Raffelt, Bernhard Steffen, Therese Berg, Tiziana Margaria. LearnLib: a framework for extrapolating behavioral models. International Journal on Software Tools for Technology Transfer, Springer Verlag, 2009, 11 (5), pp.393-407. 〈10.1007/s10009-009-0111-8〉. 〈inria-00459959〉

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