Augmentation Method of Test Data for Path Coverage based on K-means Clustering

Regression testing is the most costly testing method. The purpose of test data augmentation method is to make full use of the original test cases to augment the test data with higher path coverage and reduce the cost of regression testing. K-means algorithm is used to cluster the test data, and the proximity was measured according to Hamming distance, so that the test data assigned to the same cluster have higher path similarity. Evaluate the intra-cluster data according to the statement coverage information, select the better data to form the initial population, use the correlation between the test data crossing path and the target path to design the fitness function and evolve to generate the augmented data. Experimental results show that compared with similar methods, the proposed method improves the path coverage by 2.19 similar to 6.62%, and the fault detection rate by 2.84 similar to 6.58%.