Application of Negative Selection Algorithm (NSA) for test data generation of path testing

Abstract

Path testing is one of the areas covered in structural testing. In this process, it is a key challenge to search for a set of test data in the whole search space to satisfy path coverage. Thus, finding an efficient method for generating test data automatically is a key issue in software testing. This paper proposed a method based on Negative Selection Algorithm (NSA) for generating test data to satisfy the path coverage criterion. The results show that NSA could reduce the number of test data generated and improve the coverage percentage, as well as enhance the efficiency of the test data generation process. To evaluate the performance of the method, results from the proposed method were compared with random testing and a previous work that used Genetic Algorithm and Ant Colony Optimization. The results demonstrate that NSA outperforms other methods in reducing the number of test data that cover all program paths even the difficult ones.