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An Empirical Study of Configuration Strategies of Evolutionary Testing


Liang Shi, Baowen Xu, Xiaoyuan Xie


Vol. 6  No. 1  pp. 44~49


Evolutionary testing is an efficient method of automated test case generation, which utilizes a kind of meta-heuristic search technique, genetic algorithm, to convert the task of test case generation into an optimal problem. The configuration strategies of genetic algorithm have notable influences upon the performance of evolutionary testing. Although there have been some analytical and empirical evaluations of evolutionary testing, to our knowledge only one study, focusing on the configuration strategies of structural testing, has been performed. We have carried out a series of experiments to examine the relative performances of different configuration strategies. The experiments focus on structural evolutionary testing that generates test data to cover a statement in a function without loop conditions. Our results highlight several differences between the configuration strategies, and provide some advices that are helpful to select a configuration strategy in practice


Software testing, evolutionary testing, genetic algorithm, empirical study