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 levy 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.