A Model-Based Test Script Generation Framework and Industrial Insight

Model-based testing (MBT) generates test cases through a model representing the software under test (SUT). The generated abstract test cases need to be transformed into concrete or executable test scripts. Despite the benefits offered by MBT, its industrial adoption is slow. This paper aims to propose a Model-Based Test scrIpt GenEration fRamework (TIGER) based on GraphWalker (GW), an open-source MBT tool, to evaluate the accuracy of generated test scripts to reflect real-world scenarios defined by the model, and to report on the findings of an industrial survey on MBT adoption. We have validated the robustness of the TIGER using an industrial case study from Alstom Rail AB, Sweden. We have injected faults into the model of the SUT based on three mutation operators to generate faulty test scripts. The aim of generating faulty test scripts is to produce failing test steps and to guarantee the absence of faults in the SUT. Moreover, we have also generated the test scripts using the correct version of the model and executed them to analyze the behavior of the generated test scripts in comparison with manually written test scripts. The experimental results show that the generated test scripts are executable, provide 100% requirements coverage, and can be used to uncover faults at the software-in-the-loop simulation level of system testing. Additionally, the analysis of the survey data reveals that MBT can address most of the identified testing challenges, but there remain certain barriers to its adoption.