Multi-Population Genetic Algorithm Based EFSM Regression Test Data Generation

Extended Finite State Machine(EFSM) has a wide range of application systems and protocols in control modeling. Genetic algorithms are commonly used in EFSM test data generation due to their good performance. Studies have been done to generate regression test sequences to meet the requirements based on changes in EFSM, there is a large amount of historical test data to choose from when using genetic algorithms to generate test data for this part of the test sequence, and there is no research on how to fully utilize the existing test data to generate regression test data for the EFSM. In this paper, a multi-population genetic algorithm is designed to generate test data to meet the requirements. Firstly, some test data that can cover the regression test sequences are selected from the historical test data, and for the rest of the test sequences, a mathematical model for the simultaneous optimization of multiple test sequences is constructed, secondly, in order to make full use of the historical test data, the relationship between the existing test data and the target test sequences is investigated, and the initial population is constructed based on the similarity of the test sequences and the FSCS-ART algorithm, which makes full use of the The initial population is constructed based on the similarity of test sequences and the FSCS-ART algorithm, which makes full use of the existing test data and ensures the diversity of the initial population. Finally, the adaptive crossover operator and variation operator were designed and the fitness function was optimized. The experimental results show the feasibility and efficiency of this method in test data generation.