A novel method of grouping target paths for parallel programs

Abstract

Genetic algorithms can be employed to automatically generate desired test data, with the advantage of freeing up manpower. For the path coverage criterion, the problem of test data generation needs to be transformed into an optimization problem before applying genetic algorithms. However, when the number of paths to be covered is large, the transformed optimization problem will be very complicated. Correspondingly, the difficulty of problem solving will be greatly increased. In view of this, the complex optimization problem is divided into a number of sub-optimization problems by grouping paths. However, the existing method of grouping paths has not fully taken the characteristic of multiple processes existing in a parallel program into consideration. As a result, inappropriate paths will be put into the same group, which heavily restricts the efficiency of test data generation. To overcome the above drawback, this study proposes a novel method of grouping paths. This method refines the measurement of the path similarity when grouping target paths, dynamically increases the number of benchmark paths in a group, and groups the remaining ones based on the similarity between a path and each of these benchmark paths, with the purpose of a large similarity between each pair of paths in the same group. The proposed method is applied to test nine typical programs, and compared with the method of randomly grouping paths and the existing method of grouping paths. The experimental results show that paths in the same group obtained by the proposed method have a larger similarity, which is beneficial to efficiently generating test data that satisfy the path coverage criterion.