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An Optimized Test Case Minimization Technique Using Genetic Algorithm for Regression Testing

Rubab Sheikh1, Muhammad Imran Babar2,*, Rawish Butt3, Abdelzahir Abdelmaboud4, Taiseer Abdalla Elfadil Eisa4

1 Bahria University, Islamabad, 46000, Pakistan
2 Department of Computer Science, FAST-National University of Computer and Emerging Sciences, Islamabad, Pakistan
3 Department of Computer Science, SZABIST, Islamabad, 46000, Pakistan
4 Department of Information Systems, College of Science and Arts, King Khalid University, Mahayil Asir, Saudi Arabia

* Corresponding Author: Muhammad Imran Babar. Email:

Computers, Materials & Continua 2023, 74(3), 6789-6806.


Regression testing is a widely used approach to confirm the correct functionality of the software in incremental development. The use of test cases makes it easier to test the ripple effect of changed requirements. Rigorous testing may help in meeting the quality criteria that is based on the conformance to the requirements as given by the intended stakeholders. However, a minimized and prioritized set of test cases may reduce the efforts and time required for testing while focusing on the timely delivery of the software application. In this research, a technique named TestReduce has been presented to get a minimal set of test cases based on high priority to ensure that the web application meets the required quality criteria. A new technique TestReduce is proposed with a blend of genetic algorithm to find an optimized and minimal set of test cases. The ultimate objective associated with this study is to provide a technique that may solve the minimization problem of regression test cases in the case of linked requirements. In this research, the 100-Dollar prioritization approach is used to define the priority of the new requirements.


Cite This Article

R. Sheikh, M. I. Babar, R. Butt, A. Abdelmaboud and T. A. Elfadil Eisa, "An optimized test case minimization technique using genetic algorithm for regression testing," Computers, Materials & Continua, vol. 74, no.3, pp. 6789–6806, 2023.

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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