Robotic Automation of Software Testing From a Machine Learning Viewpoint
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Date
2021-12-21
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Automation and Computer Science, Brno University of Technology
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Abstract
The need to scale software test automation while managing the test automation process within a reasonable time frame remains a crucial challenge for software development teams (DevOps). Unlike hardware, the software cannot wear out but can fail to satisfy the functional requirements it is supposed to meet due to the defects observed during system operation. In this era of big data, DevOps teams can deliver better and efficient code by utilizing machine learning (ML) to scan their new codes and identify test coverage gaps. While still in its infancy, the inclusion of ML in software testing is a reality and requirement for coming industry demands. This study introduces the prospects of robot testing and machine learning to manage the test automation process to guarantee software reliability and quality within a reasonable timeframe. Although this paper does not provide any particular demonstration of ML-based technique and numerical results from ML-based algorithms, it describes the motivation, possibilities, tools, components, and examples required for understanding and implementing the robot test automation process approach.
Description
Citation
Mendel. 2021 vol. 27, č. 2, s. 68-73. ISSN 1803-3814
https://mendel-journal.org/index.php/mendel/article/view/160
https://mendel-journal.org/index.php/mendel/article/view/160
Document type
Peer-reviewed
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Published version
Date of access to the full text
Language of document
en
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Date of acceptance
Defence
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Document licence
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license
http://creativecommons.org/licenses/by-nc-sa/4.0
http://creativecommons.org/licenses/by-nc-sa/4.0