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Knowledge-based relevance filtering for efficient system-level test-based model generation

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Abstract

Test-based model generation by classical automata learning is very expensive. It requires an impractically large number of queries to the system, each of which must be implemented as a system-level test case. Key in the tractability of observation-based model generation are powerful optimizations exploiting different kinds of expert knowledge in order to drastically reduce the number of required queries, and thus the testing effort. In this paper, we present a thorough experimental analysis of the second-order effects between such optimizations in order to maximize their combined impact.

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Authors and Affiliations

  1. Chair of Service Engineering for Distributed Systems, Universität Göttingen, Germany

    Tiziana Margaria & Bernhard Steffen

  2. Lehrstuhl für Programmiersysteme, Universität Dortmund, Germany

    Tiziana Margaria

  3. METAFrame Technologies GmbH, Dortmund, (Germany)

    Harald Raffelt & Bernhard Steffen

Authors
  1. Tiziana Margaria
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  2. Harald Raffelt
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  3. Bernhard Steffen
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Correspondence to Tiziana Margaria.

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Margaria, T., Raffelt, H. & Steffen, B. Knowledge-based relevance filtering for efficient system-level test-based model generation. Innovations Syst Softw Eng 1, 147–156 (2005). https://doi.org/10.1007/s11334-005-0016-y

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  • DOI: https://doi.org/10.1007/s11334-005-0016-y

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