Model-Based Testing of Probabilistic Systems with Stochastic Time

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    2 Citations (Scopus)


    This paper presents a model-based testing framework for black-box probabilistic systems with stochastic continuous time. Markov automata are used as an underlying model. We show how to generate, execute and evaluate test cases automatically from a probabilistically timed requirements model. In doing so, we connect classical ioco-theory with statistical hypothesis testing; our ioco-style algorithms test for functional behaviour, while χ2 hypothesis tests and confidence interval estimations assess the statistical correctness of the system.

    A crucial development are the classical soundness and completeness properties of our framework. Soundness states that test cases assign the correct verdict, while completeness states that our methods are powerful enough to discover each discrepancy in functional or statistical misbehaviour, up to arbitrary precision.

    We illustrate our framework via the Bluetooth device discovery protocol.
    Original languageEnglish
    Title of host publicationTests and Proofs
    Subtitle of host publication11th International Conference, TAP 2017, Held as Part of STAF 2017, Marburg, Germany, July 19–20, 2017, Proceedings
    EditorsEinar Broch Johnsen
    ISBN (Electronic)978-3-319-61467-0
    Publication statusPublished - 18 Jun 2017
    Event11th International Conference on Tests & Proofs - Marburg, Germany
    Duration: 19 Jul 201720 Jul 2017
    Conference number: 11

    Publication series

    NameLecture notes in computer science


    Conference11th International Conference on Tests & Proofs
    Abbreviated titleTAP 2017
    Internet address

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