A Hybrid PSO Approach to Automate Test Data Generation for Data Flow Coverage with Dominance Concepts

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This paper presents a technique that based on a combination of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), and is thus called GPSCA (Genetic-Particle Swarm Combined Algorithm) which is used to generate automatic test data for data flow coverage with using dominance concept between two nodes. The performance of the proposed approach is analyzed on a number of programs having different size and complexity. Finally, the performance of GPSCA is compared to both GA and PSO for generation of automatic test cases to demonstrate its superiority.