Model Learning and Model-Based Testing

Bernhard Aichernig, Wojciech Mostowski, Mohammad Reza Mousavi, Martin Tappler, Masoumeh Taromirad

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

Abstract

We present a survey of the recent research efforts in integrating model learning with model-based testing. We distinguished two strands of work in this domain, namely test-based learning (also called test-based modeling) and learning-based testing. We classify the results in terms of their underlying models, their test purpose and techniques, and their target domains.
Original languageEnglish
Title of host publicationMachine Learning for Dynamic Software Analysis: Potentials and Limits
Subtitle of host publicationInternational Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016, Revised Papers
EditorsAmel Bennaceur, Reiner Hähnle, Karl Meinke
Place of PublicationCham
PublisherSpringer Nature
Pages74 - 100
Number of pages27
ISBN (Electronic)978-3-319-96562-8
ISBN (Print)978-3-319-96561-1
DOIs
Publication statusPublished - 20 Jul 2018
EventInternational Dagstuhl Seminar 1617 - Schloß Dagstuhl, Wadern, Germany
Duration: 24 Apr 201627 Apr 2016

Publication series

NameLecture Notes in Computer Science
Volume11026

Conference

ConferenceInternational Dagstuhl Seminar 1617
CountryGermany
CityWadern
Period24/04/1627/04/16

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Aichernig, B., Mostowski, W., Mousavi, M. R., Tappler, M., & Taromirad, M. (2018). Model Learning and Model-Based Testing. In A. Bennaceur, R. Hähnle, & K. Meinke (Eds.), Machine Learning for Dynamic Software Analysis: Potentials and Limits: International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016, Revised Papers (pp. 74 - 100). (Lecture Notes in Computer Science; Vol. 11026). Cham: Springer Nature. https://doi.org/10.1007/978-3-319-96562-8_3