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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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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