Revealing influence of model structure and test case profile on the prioritization of test cases in the context of model-based testing

BackgroundTest case prioritization techniques aim at defining an order of test cases that favor the achievement of a goal during test execution, such as revealing failures as earlier as possible. A number of techniques have already been proposed and investigated in the literature and experimental results have discussed whether a technique is more successful than others. However, in the context of model-based testing, only a few attempts have been made towards either proposing or experimenting test case prioritization techniques. Moreover, a number of factors that may influence on the results obtained still need to be investigated before more general conclusions can be reached.MethodsIn order to evaluate factors that potentially affect the performance of test case prioritization techniques, we perform three empirical studies, an exploratory one and two experiments. The first study focus on expose the techniques to a common and fair environment, since the investigated techniques have never been studied together, and observe their behavior. The following two experiments aim at observing the effects of two factors: the structure of the model and the profile of the test cases that fail. We designed the experiments using the one-factor-at-a-time strategy.ResultsThe first study suggests that the investigated techniques performs differently, however other factors, aside from the test suites and number of failures, affect the techniques, motivating further investigation. As results from the two experiments, on one hand, the model structure do not affect significantly the investigated techniques. On the other hand, we are able to state that the profile of the test case that fails may have a definite influence on the performance of the techniques investigated.ConclusionsThrough these studies, we conclude that, a fair evaluation involving test case prioritization techniques must take into account, in addition to the techniques and the test suites, different characteristics of the test cases that fail as variable.