A Machine Learning Based Methodology for Web Systems Codeless Testing with Selenium

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

Abstract

Web system testing is a crucial software development cycle. However, though there are real needs for testing these complex systems, it often requires specific skills in testing and/or technical programming. Moreover, the lifecycles of web systems are today very dynamic. They are often modified, updated, integrating new data, links, widgets, etc. Therefore, the testing processes and scripts for these systems have to be modified as well which can be very costly in terms of time and resources. Based on that context, this paper aims at reducing these prerequisites and constraints for tester in proposing a codeless testing automation framework. Our approach is based on Selenium and a machine learning technique to propose generic testing scripts that can be automatically tuned to the tested use cases. Experiments are provided leading to relevant results demonstrating the success of our methodology.

Original languageEnglish
Title of host publicationSoftware Technologies - 15th International Conference, ICSOFT 2020, Revised Selected Papers
EditorsMarten van Sinderen, Leszek A. Maciaszek, Hans-Georg Fill
PublisherSpringer Science and Business Media Deutschland GmbH
Pages184-202
Number of pages19
ISBN (Print)9783030830069
DOIs
Publication statusPublished - 1 Jan 2021
Event15th International Conference on Software Technologies, ICSOFT 2020 - Virtual, Online, France
Duration: 7 Jul 20209 Jul 2020

Publication series

NameCommunications in Computer and Information Science
Volume1447
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference15th International Conference on Software Technologies, ICSOFT 2020
Country/TerritoryFrance
CityVirtual, Online
Period7/07/209/07/20

Keywords

  • Automation testing
  • Codeless web testing
  • Machine learning
  • SVM
  • Selenium

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