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Optimal Test Case Selection Using Ant Colony and Rough Sets

Optimal Test Case Selection Using Ant Colony and Rough Sets

Angelin Gladston, Niranjana Devi N.
Copyright: © 2020 |Volume: 11 |Issue: 2 |Pages: 14
ISSN: 1942-3594|EISSN: 1942-3608|EISBN13: 9781799806226|DOI: 10.4018/IJAEC.2020040101
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MLA

Gladston, Angelin, and Niranjana Devi N. "Optimal Test Case Selection Using Ant Colony and Rough Sets." IJAEC vol.11, no.2 2020: pp.1-14. http://doi.org/10.4018/IJAEC.2020040101

APA

Gladston, A. & Niranjana Devi N. (2020). Optimal Test Case Selection Using Ant Colony and Rough Sets. International Journal of Applied Evolutionary Computation (IJAEC), 11(2), 1-14. http://doi.org/10.4018/IJAEC.2020040101

Chicago

Gladston, Angelin, and Niranjana Devi N. "Optimal Test Case Selection Using Ant Colony and Rough Sets," International Journal of Applied Evolutionary Computation (IJAEC) 11, no.2: 1-14. http://doi.org/10.4018/IJAEC.2020040101

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Abstract

Test case selection helps in improving quality of test suites by removing ambiguous, redundant test cases, thereby reducing the cost of software testing. Various works carried out have chosen test cases based on single parameter and optimized the test cases using single objective employing single strategies. In this article, a parameter selection technique is combined with an optimization technique for optimizing the selection of test cases. A two-step approach has been employed. In first step, the fuzzy entropy-based filtration is used for test case fitness evaluation and selection. In second step, the improvised ant colony optimization is employed to select test cases from the previously reduced test suite. The experimental evaluation using coverage parameters namely, average percentage statement coverage and average percentage decision coverage along with suite size reduction, demonstrate that by using this proposed approach, test suite size can be reduced, reducing further the computational effort incurred.

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