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Improved Test Case Selection Algorithm to Reduce Time in Regression Testing

Israr Ghani*, Wan M. N. Wan-Kadir, Adila Firdaus Arbain, Noraini Ibrahim

School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, 81310, Malaysia

* Corresponding Author: Israr Ghani. Email: email

Computers, Materials & Continua 2022, 72(1), 635-650. https://doi.org/10.32604/cmc.2022.025027

Abstract

Regression testing (RT) is an essential but an expensive activity in software development. RT confirms that new faults/errors will not have occurred in the modified program. RT efficiency can be improved through an effective technique of selected only modified test cases that appropriate to the modifications within the given time frame. Earlier, several test case selection approaches have been introduced, but either these techniques were not sufficient according to the requirements of software tester experts or they are ineffective and cannot be used for available test suite specifications and architecture. To address these limitations, we recommend an improved and efficient test case selection (TCS) algorithm for RT. Our proposed technique decreases the execution time and redundancy of the duplicate test cases (TC) and detects only modified changes that appropriate to the modifications in test cases. To reduce execution time for TCS, evaluation results of our proposed approach are established on fault detection, redundancy and already executed test case. Results indicate that proposed technique decreases the inclusive testing time of TCS to execute modified test cases by, on average related to a method of Hybrid Whale Algorithm (HWOA), which is a progressive TCS approach in regression testing for a single product.

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Cite This Article

I. Ghani, W. M. N. Wan-Kadir, A. Firdaus Arbain and N. Ibrahim, "Improved test case selection algorithm to reduce time in regression testing," Computers, Materials & Continua, vol. 72, no.1, pp. 635–650, 2022.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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