Prioritization Method of Test Data for Intelligent Software based on Multi-objective Optimization

With the rapid development of the information industry, intelligent software testing has become one of the hot research. This paper studies how to extract useful data from the original test set to test the modified model for the intelligent software. First, the initial test data are obtained according to the label classification of the original structured documents. Second, the non-dominated sorting genetic algorithm is used to prioritize the test data. Finally, the experimental results of intelligent software show that compared with the random method, the accuracy of the penalty_money prediction model is reduced by 0.04%~ 34.91 %, and the accuracy of the penalty_imprisonment prediction model is reduced by 0.08% ~ 32.63 %, indicating that the proposed method can find more defects of the penalty prediction model earlier, to reduce the cost of intelligent software testing.