Adaptive Learning for Learn-Based Regression Testing

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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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.
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
PublisherSpringer
Pages162-177
Number of pages16
ISBN (Electronic)978-3-030-00244-2
ISBN (Print)978-3-030-00243-5
DOIs
Publication statusPublished - Sep 2018
Event23rd International Conference on Formal Methods for Industrial Critical Systems 2018 - Maynooth University, Maynooth, Ireland
Duration: 3 Sep 20184 Sep 2018
Conference number: 23
http://fmics2018.fi.muni.cz/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11119

Conference

Conference23rd International Conference on Formal Methods for Industrial Critical Systems 2018
Abbreviated titleFMICS 2018
CountryIreland
CityMaynooth
Period3/09/184/09/18
Internet address

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Testing
Model checking
Experiments

Cite this

Huistra, D., Meijer, J., & Pol, J. V. D. (2018). Adaptive Learning for Learn-Based Regression Testing. In F. Howar, & J. Barnat (Eds.), Formal Methods for Industrial Critical Systems: 23rd International Conference, FMICS 2018, Maynooth, Ireland, September 3-4, 2018, Proceedings (pp. 162-177). (Lecture Notes in Computer Science; Vol. 11119). Springer. https://doi.org/10.1007/978-3-030-00244-2_11
Huistra, David ; Meijer, Jeroen ; Pol, Jaco van de. / Adaptive Learning for Learn-Based Regression Testing. Formal Methods for Industrial Critical Systems: 23rd International Conference, FMICS 2018, Maynooth, Ireland, September 3-4, 2018, Proceedings. editor / Falk Howar ; Jiri Barnat. Springer, 2018. pp. 162-177 (Lecture Notes in Computer Science).
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Huistra, D, Meijer, J & Pol, JVD 2018, Adaptive Learning for Learn-Based Regression Testing. in F Howar & J Barnat (eds), Formal Methods for Industrial Critical Systems: 23rd International Conference, FMICS 2018, Maynooth, Ireland, September 3-4, 2018, Proceedings. Lecture Notes in Computer Science, vol. 11119, Springer, pp. 162-177, 23rd International Conference on Formal Methods for Industrial Critical Systems 2018, Maynooth, Ireland, 3/09/18. https://doi.org/10.1007/978-3-030-00244-2_11

Adaptive Learning for Learn-Based Regression Testing. / Huistra, David; Meijer, Jeroen; Pol, Jaco van de.

Formal Methods for Industrial Critical Systems: 23rd International Conference, FMICS 2018, Maynooth, Ireland, September 3-4, 2018, Proceedings. ed. / Falk Howar; Jiri Barnat. Springer, 2018. p. 162-177 (Lecture Notes in Computer Science; Vol. 11119).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Huistra D, Meijer J, Pol JVD. Adaptive Learning for Learn-Based Regression Testing. In Howar F, Barnat J, editors, Formal Methods for Industrial Critical Systems: 23rd International Conference, FMICS 2018, Maynooth, Ireland, September 3-4, 2018, Proceedings. Springer. 2018. p. 162-177. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-030-00244-2_11