Latin hypercube sampling Jaya algorithm based strategy for T-way test suite generation

• Main Authors: Nasser, Abdullah B., Abdul-Qawy, Antar S. H., Abdullah, Nibras, Hujainah, Fadhl, Kamal Z., Zamli, Ghanem, Waheed A. H. M.
• Format: Conference or Workshop Item
• Language: English
• Published: Association for Computing Machinery 2020
• Subjects: QA76 Computer software
• Online Access: http://umpir.ump.edu.my/id/eprint/32294/1/Latin%20hypercube%20sampling%20Jaya%20algorithm%20based%20strategy%20for%20T-way%20.pdf; http://umpir.ump.edu.my/id/eprint/32294/; https://doi.org/10.1145/3384544.3384608

T-way testing is a sampling strategy that generates a subset of test cases from a pool of possible tests. Many t-way testing strategies appear in the literature to-date ranging from general computational ones to meta-heuristic based. Owing to its performance, man the meta-heuristic based t-way strategies have gained significant attention recently (e.g. Particle Swarm Optimization, Genetic Algorithm, Ant Colony Algorithm, Harmony Search, Jaya Algorithm and Cuckoo Search). Jaya Algorithm (JA) is a new metaheuristic algorithm, has been used for solving different problems. However, losing the search's diversity is a common issue in the metaheuristic algorithm. In order to enhance JA's diversity, enhanced Jaya Algorithm strategy called Latin Hypercube Sampling Jaya Algorithm (LHS-JA) for Test Suite Generation is proposed. Latin Hypercube Sampling (LHS) is a sampling approach that can be used efficiently to improve search diversity. To evaluate the efficiency of LHS-JA, LHS-JA is compared against existing metaheuristic-based t-way strategies. Experimental results have shown promising results as LHS-JA can compete with existing t-way strategies.