A Hybrid Model of Particle Swarm and Ant Colony Optimization Algorithm for Test Case Optimization

Regression testing is the process of validating modifications introduced in a system during software maintenance. It is done to check that a system update does not introduce errors that have been corrected or the change in one part of the program does not affect the other modules of that program. As the test suite is very large, system retesting consumes large amount of time and computing resources. In that scenario we use test case prioritization. In this approach the testers sort out the test cases such that those with higher priorities are run earlier than those with lower priorities. The purpose of this prioritization is to increase the likelihood that if the test cases are used for regression testing in the given order, they will more closely meet some objectives than they would if they were executed in some other order. In this paper a hybrid approach has been proposed that is divided into four phases. The first being clustering based on requirement prioritization, second being IntraCluster Prioritization, next being Test Case Selection and Inter-Cluster Prioritization. In the second phase i.e. Intra-Cluster Prioritization PSACO (PSO+ACO) algorithm has been used. The reason for combining PSO (Particle Swarm Optimization) with ACO (Ant Colony Optimization) is the guaranteed convergence of ACO. Also properties of low constraint on the continuity of objective function and ability of adapting to the dynamic environment make PSO one of the most important swarm intelligence algorithms. Due to these properties the proposed approach combines the strengths of PSO and ACO to give an optimized test suite.