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In search based test data generation, the problem of test data generation is reduced to that of function minimization or maximization. Traditionally, for branch testing, the problem of test data generation has been formulated as a minimization problem. In this paper, the authors define an alternate maximization formulation and experimentally compare it with the minimization formulation. They use genetic algorithm and binary particle swarm optimization as the search technique and in addition to the usual operators they also employ a branch ordering strategy, memory and elitism.
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