EFSM-based test data generation with Multi-Population Genetic Algorithm

Abstract

Extended Finite State Machine (EFSM) is a popular formal specification which is widely used to describe states and actions of software system. Automated test generation on EFSM model is difficult due to the existence of the context variables. Multi-Population Genetic Algorithm (MPGA) is a novel heuristic search algorithm which is introduced to automatically generate test data for transition paths on EFSM models. Meanwhile, the parameter setting of MPGA is a critical problem for the efficiency of test data generation. A simple `rules of thumb' approach is applied to find an optimal parameter setting of MPGA on test data generation for EFSM models. The experimental results suggest that MPGA can effectively generate test data for the transition paths of EFSM models and the optimal parameters setting obtained by `rules of thumb' can ensure the efficiency of test data automatic generation for EFSM models.