Model-based test case generation has become a hotspot, and automatic generation of test data is difficult in this area. In this paper, system model is represented by extended finite state machine(EFSM), and genetic algorithm is used to generate test data for EFSM paths. When computing the fitness of an individual, the branch distance and the ratio of uncovered conditions of the individual are considered. In experiments, the proposed method is compared with the Kalaji's, and the results show that our method has a better effect and can get higher quality test data.
Lu, G., & Miao, H. (2014). An approach to generating test data for EFSM paths considering condition coverage. In Electronic Notes in Theoretical Computer Science (Vol. 309, pp. 13–29). Elsevier. https://doi.org/10.1016/j.entcs.2014.12.003