An evaluation of Differential Evolution in software test data generation

One of the main tasks software testing involves is the generation of the test inputs to be used during the test. Due to its expensive cost, the automation of this task has become one of the key issues in the area. Recently, this generation has been explicitly formulated as the resolution of a set of constrained optimisation problems. Differential Evolution (DE) is a population based evolutionary algorithm which has been successfully applied in a number of domains, including constrained optimisation. We present a test data generator employing DE to solve each of the constrained optimisation problems, and empirically evaluate its performance for several DE models. With the aim of comparing this technique with other approaches, we extend the experiments to the Breeder Genetic Algorithm and face it to DE, and compare different test data generators in the literature with the DE approach. The results present DE as a promising solution technique for this real-world problem.

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