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International Workshop on Formal Methods for Industrial Critical Systems

FMICS 2018: Formal Methods for Industrial Critical Systems pp 162–177Cite as

Adaptive Learning for Learn-Based Regression Testing

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11119))

Abstract

Regression testing is an important activity to prevent the introduction of regressions into software updates. Learn-based testing can be used to automatically check new versions of a system for regressions on a system level. This is done by learning a model of the system and model checking this model for system property violations.

Learning the model of a large system can take an unpractical amount of time however. In this work we investigate if the concept of adaptive learning can improve the learning speed of a model in a regression testing scenario.

We have performed several experiments with this technique on two systems: ToDoMVC and SSH. We find that there can be a large benefit to using adaptive learning. In addition we find three main factors that influence the benefit of adaptive learning. There are however also some shortcomings to adaptive learning that should be investigated further.

D. Huistra, J. Meijer—Supported by STW SUMBAT grant: 13859.

J. van de Pol—Supported by the 3TU.BSR project.

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Notes

  1. 1.

    http://todomvc.com/.

  2. 2.

    https://matt.ucc.asn.au/dropbear/dropbear.html.

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Authors and Affiliations

  1. Formal Methods and Tools, University of Twente, Enschede, The Netherlands

    David Huistra, Jeroen Meijer & Jaco van de Pol

Authors
  1. David Huistra
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  2. Jeroen Meijer
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  3. Jaco van de Pol
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Corresponding author

Correspondence to David Huistra .

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Editors and Affiliations

  1. Technical University of Dortmund, Dortmund, Germany

    Falk Howar

  2. Masaryk University, Brno, Czech Republic

    Jiří Barnat

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Huistra, D., Meijer, J., van de Pol, J. (2018). Adaptive Learning for Learn-Based Regression Testing. In: Howar, F., Barnat, J. (eds) Formal Methods for Industrial Critical Systems. FMICS 2018. Lecture Notes in Computer Science(), vol 11119. Springer, Cham. https://doi.org/10.1007/978-3-030-00244-2_11

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  • DOI: https://doi.org/10.1007/978-3-030-00244-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00243-5

  • Online ISBN: 978-3-030-00244-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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