Efficient Generation of Test Cases for MPI Program Path Coverage through Elite Individual Selection

In the field of message-passing interface (MPI) program path coverage test case generation, evolutionary algorithms (EAs) have been frequently utilized to generate test cases. However, relying solely on EAs will incur excessive computational costs. In this article, we improve the efficiency and quality of MPI program path coverage test cases generated by EAs based on elite individual selection. First, data within the data domain is sampled and fitness is calculated to form a shared set. Then, the population data is initialized using EAs, and the fitness of individuals is predicted using the neighbor value sharing algorithm (NVSA). Subsequently, individuals are ranked using rank-based elite selection (RES). Finally, elite individuals are chosen through ranking to run the program and verify the generation of test cases. In order to reduce computational costs, data dimensionality reduction operations are added to the above process. We demonstrate that the proposed method can effectively generate test data and reduce test costs by comparing it with several excellent methods on seven representative MPI programs. Among them, NVSA has a maximum improvement of 42.2%, RES has a maximum improvement of 31.5%, dimensionality reduction can increase by 20.2%, and the overall method has a maximum improvement of 47.4%.