Test Data Generation for Path Coverage of MPI Programs Using SAEO

Abstract

Message-passing interface (MPI) programs, a typical kind of parallel programs, have been commonly used in various applications. However, it generally takes exhaustive computation to run these programs when generating test data to test them. In this article, we propose a method of test data generation for path coverage of MPI programs using surrogate-assisted evolutionary optimization, which can efficiently generate test data with high quality. We first divide a sample set of a program into a number of clusters according to the multi-mode characteristic of the coverage problem, with each cluster training a surrogate model. Then, we estimate the fitness of each individual using one or more surrogate models when generating test data through evolving a population. Finally, a small number of representative individuals are selected to execute the program, with the purpose of obtaining their real fitness, to guide the subsequent evolution of the population. We apply the proposed method to seven benchmark MPI programs and compare it with several state-of-the-art approaches. The experimental results show that the proposed method can generate test data with reduced computation, thus improving the testing efficiency.