An Evolutionary Generation Method of Test Data for Multiple Paths Based on Coverage Balance

Test data generation is one of the main tasks of software testing. The goal of test data generation based on search algorithms is to automate the task and find test data that meet test criteria. In this study, an evolutionary generation method for test data that cover multiple paths is proposed. Firstly, the method obtains the coverage balance for each target path based on the number of individuals traversing the true and false branches of branch nodes, and calculates the individual's influence on coverage balance before and after an individual joining based on our previous work. Then, according to the number of branch nodes on each target path, the weights of different target paths are designed to obtain the individual fitness to adjust the evolution process and quickly generate test data covering multiple target paths. Finally, the proposed method is compared with existing techniques. Experimental results of benchmark programs and industrial use cases show that the proposed method can effectively improve the efficiency of test data generation for multiple paths.