Investigating The Effectiveness of Model-Based Testing on Testing Skill Acquisition.

Software does not only need to be developed but also needs to get tested. Testing of software reduces the development and maintenance costs and increases software quality. Unfortunately, few software development courses focus on good testing practices. Some prior work has nevertheless researched possible ways of teaching software testing techniques to students. Unfortunately, all these approaches are code-oriented approaches, implying that a strong technical background is needed to effectively use them. They are also mostly focused on improving students' knowledge on basic testing techniques. In contrast, TesCaV, a software tool used for teaching testing skills to university students, focuses on teaching testing to novice users with limited technical skills by providing a model-based testing (MBT) approach. MBT is a black-box testing technique in which the tests are automatically generated from a software model. This automatic generation allows for easy maintenance of the test suite when the software changes. These tests can be automatically generated by using a.o. Finite State Machines (FSMs), Markov Chains and Logic Programming. TesCaV is mainly based on Finite State Machines. The effect of using TesCaV on testing coverage is quantitatively analysed in this research. The preliminary results of TesCaV show that it is a promising tool for the teaching of MBT.