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Title: Community detection in model-based testing to address scalability: Study design
Authors: Silistre, Alper
Kılınççeker, Onur
Belli, Fevzi
Challenger, Moharram
Kardaş, Geylani
Issue Date: 2020
Publisher: Institute of Electrical and Electronics Engineers
Abstract: Model-based GUI testing has achieved widespread recognition in academy thanks to its advantages compared to code-based testing due to its potentials to automate testing and the ability to cover bigger parts more efficiently. In this study design paper, we address the scalability part of the model-based GUI testing by using community detection algorithms. A case study is presented as an example of possible improvements to make a model-based testing approach more efficient. We demonstrate layered ESG models as an example of our approach to consider the scalability problem. We present rough calculations with expected results, which show 9 times smaller time and space units for 100 events in the ESG model when a community detection algorithm is applied. © 2020 Polish Information Processing Society - as it is since 2011.
ISBN: 978-839554167-4
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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