Papers Papers/2020



Authors: Duyen Nguyen and Stephane Maag

Affiliation: Samovar, CNRS, Télécom SudParis, Institut Polytechnique de Paris, France

Keyword(s): Codeless Testing, Web Testing, Automation Testing, Selenium, Machine Learning, SVM.

Abstract: Complexity of web systems lead to development processes always more tough to test. Testing phases are crucial in software and system engineering and are known to be very costly. While automated testing methods appear to take over the role of the human testers, the issues of reliability and the capability of the testing method still need to be solved. In our paper, we focus on the automation of functional tests of websites. A single web page may contain a huge set of important functionalities leading to the execution of critical web service operations. Besides, testing all of these functionalities implemented in a web page service is highly complex. Two current popular research areas for automation web-based testing are Codeless Functional Test Automation and Machine Learning/Artificial Intelligence (ML/AI) in test automation. We therefore define and implement a framework to figure out how to automate the web service product under test, the machine can detect or predict the change and adapt those changes to suitable generic test cases. In our work, we examine on Selenium and the benefits of using machine learning in automated web application testing. (More)


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Paper citation in several formats:
Nguyen, D. and Maag, S. (2020). Codeless Web Testing using Selenium and Machine Learning. In Proceedings of the 15th International Conference on Software Technologies - ICSOFT, ISBN 978-989-758-443-5; ISSN 2184-2833, pages 51-60. DOI: 10.5220/0009885400510060

author={Duyen Nguyen. and Stephane Maag.},
title={Codeless Web Testing using Selenium and Machine Learning},
booktitle={Proceedings of the 15th International Conference on Software Technologies - ICSOFT,},


JO - Proceedings of the 15th International Conference on Software Technologies - ICSOFT,
TI - Codeless Web Testing using Selenium and Machine Learning
SN - 978-989-758-443-5
IS - 2184-2833
AU - Nguyen, D.
AU - Maag, S.
PY - 2020
SP - 51
EP - 60
DO - 10.5220/0009885400510060