Generalized Extremal Optimization: a Competitive Algorithm for Test Data Generation

Bruno T. de AbreuEliane MartinsFabiano L. de Sousa

Software testing is an important part of the software development process, and automating test data generation contributes to reducing cost and time efforts. It has recently been shown that evolutionary algorithms (EAs), such as the Genetic Algorithms (GAs), are valuable tools for test data generation. This work assesses the performance of a recently proposed EA, the Generalized Extremal Optimization (GEO), on test data generation for programs that have paths with loops. Benchmark programs were used as study cases and GEO's performance was compared to the one of a GA. Results showed that using GEO required much less computational effort than GA on test data generation and also on internal parameter setting. These results indicate that GEO is an attractive option to be used for test data generation.

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