Abstract:
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 we define an alternate maximization formulation
and experimentally compare it with the minimization formulation. We use genetic algorithm and binary
particle swarm optimization as the search technique and in addition to the usual operators we also employ
a branch ordering strategy, memory and elitism. Results indicate that there is no significant difference in
the performance or the coverage obtained through the two approaches and either could be used in test data
generation when coupled with the branch ordering strategy, memory and elitism.