Generation method of small-scale EFSM test sequence suite for all transitions

Abstract:

In order to address the issues of low efficiency and large scale of test sequence suite on the extended finite state machine (EFSM) model, a method of small-scale EFSM test sequence suite generation for all transitions was proposed based on the improved adaptive multi-population genetic algorithm (IAMGA). Firstly, the fitness function was designed by the transition coverage gain so that each generated feasible transition path can improve transition coverage. Next, the individual was selected within the subpopulations divided by feasible transition paths, which can overcome the premature convergence and improve the success rate of all transitions coverage. Then, the probabilities of crossover and mutation were adaptively adjusted based on the average path pass rate of the population so as to speed up convergence. Finally, by traversing the test sequence suite in reverse order, redundant sequences were removed, which further reduced the size of the test sequence suite. The experimental results show that compared with the generation test sequence method for single transition, this method which is designed for all transitions, can generate a test sequence suite that meets all transitions coverage with a success rate of more than 90% at a time. Compared with the traditional genetic algorithm, the average size of test sequence suite generated by IAMGA is reduced by 50%, while the average number of iterations is reduced by 20%. The method proposed in this paper can effectively improve the efficiency and quality of the generating EFSM test sequence suite.