Genetic algorithm based test data generation for MPI parallel programs with blocking communication

Abstract

Parallel computing is one of mainstream techniques for high-performance computation in which MPI parallel programs have gained more and more attention. Genetic algorithms (GAs) have been widely employed in automated test data generation, leading to a major family of search-based software testing techniques. However, previous GA-based methods have limitations when testing MPI parallel programs with blocking communication. In this paper, we focus on the path coverage problem for MPI parallel programs with blocking communication, and formulate the problem as an optimization problem with its decision variable being the program input and the execution order of sending nodes. In addition, we develop target amending strategies for candidates when solving the problem using genetic algorithms. The proposed method is evaluated and compared with several state-of-the-art methods through a series of controlled experiments on five typical programs. The experimental results show that the proposed method can effectively and efficiently generate test data for path coverage.