Test Data Generation Algorithm of Combinatorial Testing Based on Differential Evolution

In this paper, we present a test data generation algorithm of combinatorial testing based on Differential Evolution, and introduce a selection and substitution based on the degree of unfinished interaction, in order to optimize the test case selected in further. An IPO-like strategy for generating the test suite is used to reduce the dimension for differential evolutionary computation in the optimization process and improve the efficiency of the algorithm. By preliminary experiments, the main parameters in DE algorithm are chosen. And in further experiments, we implement the algorithm in some typical instances to verify its effectiveness. Compare to other well-known algorithms, the final empirical results show the competitiveness of our algorithm in test suite size and running time.