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Model-Driven Active Automata Learning with LearnLib Studio

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Leveraging Applications of Formal Methods, Verification, and Validation (ISoLA 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 683))

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Abstract

We present our reboot of LearnLib Studio, formerly being a part of the Next Generation LearnLib (NGLL) framework for model-based construction of automata learning solutions. The new version of LearnLib Studio is a from-scratch re-implementation, which is based on an improved open-source realization of LearnLib as well as our latest version of the jABC framework (jABC4) for model-driven, service-oriented development of applications with recently added support for type-safe higher-order process modeling. Our all new version of LearnLib Studio provides an easy way to enable even users who do not necessarily have programming expertise to use and extend dedicated learning solutions with minimal manual effort. We illustrate the tool by applying automata learning to a concrete web service following the Representational State Transfer (REST) paradigm.

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Notes

  1. 1.

    http://www.learnlib.de.

  2. 2.

    http://www.graphviz.org/.

  3. 3.

    http://aide.codeplex.com/.

  4. 4.

    http://maven.apache.org.

  5. 5.

    http://hope.scce.info.

  6. 6.

    http://www.seleniumhq.org/.

  7. 7.

    http://eclipse.org/jetty/.

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Authors and Affiliations

  1. TU Dortmund University, 44221, Dortmund, Germany

    Oliver Bauer, Johannes Neubauer & Malte Isberner

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  1. Oliver Bauer
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  2. Johannes Neubauer
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  3. Malte Isberner
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Correspondence to Johannes Neubauer .

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Editors and Affiliations

  1. Lero - Irish Software Research Center, University of Limerick, Limerick, Ireland

    Anna-Lena Lamprecht

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Bauer, O., Neubauer, J., Isberner, M. (2016). Model-Driven Active Automata Learning with LearnLib Studio . In: Lamprecht, AL. (eds) Leveraging Applications of Formal Methods, Verification, and Validation . ISoLA 2016. Communications in Computer and Information Science, vol 683. Springer, Cham. https://doi.org/10.1007/978-3-319-51641-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-51641-7_8

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