Pairwise testing strategies are used to select test cases from a large search space considering the interactions of test input parameters in order to minimize the test suite size. We normally want that all 2-way interactions of parameters values occur in the test suit at least once. Due to the large and complex search space in the interaction problems, different techniques have been used to deal with this search space. Artificial intelligent techniques have been regarded as being especially adequate search strategies, since they are able to deal with search for optimization. Two of the well known algorithms are Genetic Algorithm (GA) and Ant Colony Algorithm (ACA). However, other heuristic search techniques have started to compete with GA and ACA such as Particle Swarm Optimization (PSO) in the context of algorithm simplicity and performance. This study presents the development of a new pairwise test data generation strategy based on PSO, called Pairwise Particle Swarm-based Test Generator (PPSTG). In doing so, this study also highlights PPSTG design as well as compares its performance in terms of test size against other existing strategies. PPSTG serves as our research vehicle to investigate the effectiveness of PSO for pairwise test data generation. The experimental results and comparisons of our strategy showed that our strategy can generate comparable results as far as the size of the test suite is concerned
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