基于无关变量分离的EFSM测试数据进化生成

Extended finite state machine (EFSM), a more accurate test model than finite state machine (FSM), has been widely used to describe dynamic behavior of system, and thus has been taken as the test model of various control flow and data flow systems. For EFSM model test, using search method to obtain test data to trigger a given test path has become a research hotspot in recent years. In order to improve the search efficiency, this paper proposed a method that originates from genetic algorithm (GA) and can automatically separate irrelevant input variables in a test path. By analyzing the relationship between variables and state transitions in EFSM and separating irrelevant input variables from the individual that does not affect the transition's guard in the sub-test path, the new method reduced the search space and enhanced the efficiency of test data generation. The experimental results on various complex benchmark EFSM models show that the success rate of the new method to generate effective test data is larger than 98.2%. Compared to the traditional genetic algorithm, the average number of iterations of the new method is reduced by 44.7%-85.9% and the average running time is reduced by 24.1%-85.5%.