COMPARATIVE EVALUATION OF A MAXIMIZATION AND MINIMIZATION APPROACH FOR TEST DATA GENERATION WITH GENETIC ALGORITHM AND BINARY PARTICLE SWARM OPTIMIZATION

In search based test data generation, the problem of test data generation is reduced to that of functionminimization or maximization.Traditionally, for branch testing, the problem of test data generation hasbeen formulated as a minimization problem. In this paper we define an alternate maximization formulationand experimentally compare it with the minimization formulation. We use genetic algorithm and binaryparticle swarm optimization as the search technique and in addition to the usual operators we also employa branch ordering strategy, memory and elitism. Results indicate that there is no significant difference inthe performance or the coverage obtained through the two approaches and either could be used in test datageneration when coupled with the branch ordering strategy, memory and elitism.