An Enhanced Set-based Evolutionary Algorithm for Generating Test Data that Cover Multiple Paths of a Parallel Program*

摘要

Although the traditional set-based evolutionary algorithm can generate test data covering multiple paths, it does not make full use of the uncertainty of parallel programs, information provided by the population during the evolution, and knowledge related to the domain, which makes the efficiency remain to be improved. This paper focuses on the problem of generating test data for multiple paths coverage of message-passing parallel programs, and proposes an enhanced set-based evolutionary algorithm that is suitable for parallel programs based on the evaluation of scheduling sequences to improve the efficiency in generating test data. We apply the proposed algorithm to nine message-passing parallel programs, and compare it with the traditional set-based evolutionary algorithm. The experimental results show that the proposed algorithm reduces the number of generations and the time consumption in test data generation.