A Classification Framework of Test Models in Model-based Testing

In model-based testing (MBT), the quality of input models and their relevance with the testing target has a direct impact on the quality of the test suite and the effectiveness of the whole testing process. Choosing inappropriate models may increase the number of MBT steps and may not fulfill the testers' expectations. In this paper, we focus on different input models of MBT and represent a classification framework for them. The classification is performed by considering their nature and testing abilities. We discuss the strengths and weaknesses of test models regarding their potential for generating test cases, and summarize the existing works in the literature based on the proposed classification framework. The aim of this paper is to improve the understanding of model-based test case generation approaches and help the testers to choose appropriate models to exploit test cases with regard to their testing goals and purposes.