Diversity-Oriented Test Suite Generation for EFSM Model

In this article, test diversity has been suggested to be a valid way to improve test suite effectiveness. Extended finite state machine (EFSM) is a widely used formal model, but little attention is paid on the test suite generation with more diversity. EFSM test suite generation involves test paths generation and test data generation. Considering the discrepancy between test paths has a more crucial impact on the diversity of test suite, compared with the difference between test data, this article, therefore, mainly concerns the test paths generation with more diversity for EFSM models. Hence, the factors that influence the discrepancy between test paths are investigated. Then based on these factors, an integrated distance metric is designed to evaluate the dissimilarity between test paths, and a diversity measurement for EFSM test suite is presented. Furthermore, a diversity-oriented test suite generation (DOTSG) method is proposed where a dissimilarity-based fitness function and diversity-oriented update strategy are adopted in traditional coverage-oriented EFSM test suite generation (COTSG) by genetic algorithm. The experimental results show that, compared to COTSG, our DOTSG can not only generate more diverse test suite to satisfy a certain coverage criteria, improving the fault detection capability of the test suite, but also decrease the evolution time cost and the size of test suite generated.