Test Case Generation from Cause-Effect Graph Based on Model Transformation
Hyun Seung Son, R. Young Chul Kim, Young B. Park · 2014
In software testing, cause-effect graph assures coverage criteria of 100% functional requirements with minimum test case. The existing test case generation from cause-effect graph implements the algorithmic approach. It has disadvantages to modify the entire program if the input model is different. In contrast, model transformation approach can flexibly implement with even a different input models. In the future, we need to study the method of automatic generation of test cases from UML Diagram. It is possible to generate the test case when mapping between cause-effect graph and UML diagram. In this paper, as a first research step, we propose the method to generate test cases from cause-effect graph based on model transformation. To implement the proposed method, we write the rules of model transformation with ATLAS Transformation Language (ATL), and execute the rules in development environment of Eclipse. The implemented tool of the proposed method can be easily extended by rewriting with the mapping rule between cause-effect graph and UML diagram. We just define the relationship between each models to generate the test case.