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Active Automata Learning in Practice

An Annotated Bibliography of the Years 2011 to 2016

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Machine Learning for Dynamic Software Analysis: Potentials and Limits

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11026))

Abstract

Active automata learning is slowly becoming a standard tool in the toolbox of the software engineer. As systems become ever more complex and development becomes more distributed, inferred models of system behavior become an increasingly valuable asset for understanding and analyzing a system’s behavior. Five years ago (in 2011) we have surveyed the then current state of active automata learning research and applications of active automata learning in practice. We predicted four major topics to be addressed in the then near future: efficiency, expressivity of models, bridging the semantic gap between formal languages and analyzed components, and solutions to the inherent problem of incompleteness of active learning in black-box scenarios. In this paper we review the progress that has been made over the past five years, assess the status of active automata learning techniques with respect to applications in the field of software engineering, and present an updated agenda for future research.

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Notes

  1. 1.

    We only provide a very brief sketch of the improved version of \(L^*\) due to Rivest and Schapire here [130]. A more detailed presentation can be found in Angluin’s original paper [13].

  2. 2.

    While the word \(w=\tilde{u}_0 \cdot v_{1}\) is a counterexample, \(\tilde{u}_m\) cannot be a counterexample (by construction of \(A_{Obs}\)), and all words \(\tilde{u}_i \cdot v_{i+1}\) with \(0 \le i \le m-1\) lead to the same state as \(\tilde{u}_m\) in \(A_{Obs}\). As a consequence, at one index \(\tilde{u}_{i-1} a_i \cdot v_{i+1} \in L\) and \(\tilde{u}_{i} \cdot v_{i+1} \not \in L\) (or vice versa).

  3. 3.

    https://learnlib.de/.

  4. 4.

    http://libalf.informatik.rwth-aachen.de/.

  5. 5.

    http://aide.codeplex.com/.

  6. 6.

    http://tomte.cs.ru.nl/.

  7. 7.

    https://github.com/lorisdanto/symbolicautomata.

  8. 8.

    https://bitbucket.org/learnlib/ralib/.

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  1. Dortmund University of Technology and Fraunhofer ISST, Dortmund, Germany

    Falk Howar

  2. Dortmund University of Technology, Dortmund, Germany

    Bernhard Steffen

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  2. Technische Universität Darmstadt, Darmstadt, Germany

    Reiner Hähnle

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    Karl Meinke

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Howar, F., Steffen, B. (2018). Active Automata Learning in Practice. In: Bennaceur, A., Hähnle, R., Meinke, K. (eds) Machine Learning for Dynamic Software Analysis: Potentials and Limits. Lecture Notes in Computer Science(), vol 11026. Springer, Cham. https://doi.org/10.1007/978-3-319-96562-8_5

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