Optimum Test Suite Using Fault-Type Coverage-Based Ant Colony Optimization Algorithm

M. Bharathi

Source Title: International Journal of Applied Metaheuristic Computing (IJAMC)13(1)

ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799885405|DOI: 10.4018/IJAMC.2022010106

MLA

Bharathi, M. "Optimum Test Suite Using Fault-Type Coverage-Based Ant Colony Optimization Algorithm." IJAMC vol.13, no.1 2022: pp.1-23. http://doi.org/10.4018/IJAMC.2022010106

APA

Bharathi, M. (2022). Optimum Test Suite Using Fault-Type Coverage-Based Ant Colony Optimization Algorithm. International Journal of Applied Metaheuristic Computing (IJAMC), 13(1), 1-23. http://doi.org/10.4018/IJAMC.2022010106

Chicago

Bharathi, M. "Optimum Test Suite Using Fault-Type Coverage-Based Ant Colony Optimization Algorithm," International Journal of Applied Metaheuristic Computing (IJAMC) 13, no.1: 1-23. http://doi.org/10.4018/IJAMC.2022010106

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Abstract

Software Product Lines(SPLs) covers a mixture of features for testing Software Application Program(SPA). Testing cost reduction is a major metric of software testing. In combinatorial testing(CT), maximization of fault type coverage and test suite reduction plays a key role to reduce the testing cost of SPA. Metaheuristic Genetic Algorithm(GA) do not offer best outcome for test suite optimization problem due to mutation operation and required more computational time. So, Fault-Type Coverage Based Ant Colony Optimization(FTCBACO) algorithm is offered for test suite reduction in CT. FTCBACO algorithm starts with test cases in test suite and assign separate ant to each test case. Ants elect best test cases by updating of pheromone trails and selection of higher probability trails. Best test case path of ant with least time are taken as optimal solution for performing CT. Hence, FTCBACO Technique enriches reduction rate of test suite and minimizes computational time of reducing test cases efficiently for CT.