A Hybrid Model of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithm for Test Case Optimization

In this paper a hybrid model called Particle Swarm Artificial Bee Colony algorithm (PSABC) has been proposed. The PSABC algorithm is a combination of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithm. PSABC has been used in this context to optimize the fitness value of a population in ABC algorithm using Particle Swarm Optimization. PSABC could be used to optimize the test suite, and integrate both ABC and PSO for a better result, as compared to their individual performance in terms of test case optimization. In PSABC, the sites are comparable to nodes in the Software under Test (SUT). While relating to the ABC algorithm, the bees here are the Test cases that adapt with time. The main objective of the bee is to find the areas with highest coverage and highest usage, so that failures could be identified at an earlier stage. The artificial bees adapt the test cases with time and the bee’s plan is to find out the areas where the nodes have higher coverage. The ABC algorithm is used to generate the optimal number of test-cases, which are sufficient to cover the paths. These paths are generated by using the Control Flow Graph (CFG), and the PSO generates the individual test cases first. This algorithm determines the node with the highest usage by a given test case. Based on the proposed hybrid approach, an optimal result for test case execution is obtained. The performance of the proposed method is evaluated and is compared with other optimization techniques such as PSO and Ant Colony Optimization (ACO).