Evolutionary generation of test data for path coverage through selecting target paths based on coverage difficulty

Abstract

In order to tackle the problem of generating test data covering paths of a message-passing parallel program,a method of evolutionarily generating test data was presented through selecting target paths based on the coverage difficulty.In the light of variables affecting apath's execution,complexities of a path's crucial conditions,and Halstead's metric,the path which is the easiest to be covered was selected as the target path to reduce the coverage difficulty.The mathematical model for generating test data was built according to the selected path.A genetic algorithm was employed to solve the above model so as to generate test data covering the target path.The proposed method was applied to generate test data for five benchmark parallel programs and compared to the existing methods.The results show that the proposed method can reduce the computation cost,and effectively generate test data for path coverage of parallel programs with the less number of evaluated individuals.