An Improved Jaya Algorithm-Based Strategy for T-Way Test Suite Generation
In the field of software testing, several meta-heuristics algorithms have been successfully used for finding an optimized t-way test suite (where t refers to covering level). T-way testing strategies adopt the meta-heuristic algorithms to generate the smallest/optimal test suite. However, the existing t-way strategies’ results show that no single strategy appears to be superior in all problems. The aim of this paper to propose a new variant of Jaya algorithm for generating t-way test suite called Improved Jaya Algorithm (IJA). In fact, the performance of meta-heuristic algorithms highly depends on the intensification and diversification capabilities. IJA enhances the intensification and diversification capabilities by introducing new operators search such lévy flight and mutation operator in Jaya Algorithm. Experimental results show that the IJA variant improves the results of original Jaya algorithm, also overcomes the problems of slow convergence of Jaya algorithm.
KeywordsT-way testing Meta-heuristics Jaya Algorithm Improved Jaya algorithm
The work reported in this paper is funded by Universiti Malaysia Pahang (UMP) grants “Optimization using Bee Colony” and “Prioritized t-way Test Suite Generation based on Chaotic Flower Pollination Algorithm”. We thank UMP for the contribution and supports.
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