Adaptive Learning for Learn-Based Regression Testing

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    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.
    Original languageEnglish
    Title of host publicationFormal Methods for Industrial Critical Systems
    Subtitle of host publication23rd International Conference, FMICS 2018, Maynooth, Ireland, September 3-4, 2018, Proceedings
    EditorsFalk Howar, Jiri Barnat
    Number of pages16
    ISBN (Electronic)978-3-030-00244-2
    ISBN (Print)978-3-030-00243-5
    Publication statusPublished - Sept 2018
    Event23rd International Conference on Formal Methods for Industrial Critical Systems, FMICS 2018 - Maynooth University, Maynooth, Ireland
    Duration: 3 Sept 20184 Sept 2018
    Conference number: 23

    Publication series

    NameLecture Notes in Computer Science


    Conference23rd International Conference on Formal Methods for Industrial Critical Systems, FMICS 2018
    Abbreviated titleFMICS 2018
    Internet address


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