Community Detection in Model-based Testing to Address Scalability: Study Design
Conference paper > Full Text · 2020
Authors: ALPER SİLİSTRE, ONUR KILINÇÇEKER, FEVZİ BELLİ, MOHARRAM CHALLENGER, Geylani KARDAŞ
Annals of Computer Science and Information Systems, Bulgaria, International, Verbal, Full Text, 2020-2020, vol 21, pp 657-660
DOI: 10.15439/2020f163
Summary
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.