A fuzzy Logic Based Approach for Model-based Regression Test Selection

Regression testing is performed to verify that previously developed functionality of a software system is not broken when changes are made to the system. Since executing all the existing test cases can be expensive, regression test selection (RTS) approaches are used to select a subset of them, thereby improving the efficiency of regression testing. Model-based RTS approaches select test cases on the basis of changes made to the models of a software system. While these approaches are useful in projects that already use model-driven development methodologies, a key obstacle is that the models are generally created at a high level of abstraction. They lack the information needed to build traceability links between the models and the coverage-related execution traces from the code-level test cases. In this paper, we propose a fuzzy logic based approach named FLiRTS, for UML model-based RTS. FLiRTS automatically refines abstract UML models to generate multiple detailed UML models that permit the identification of the traceability links. The process introduces a degree of uncertainty, which is addressed by applying fuzzy logic based on the refinements to allow the classification of the test cases as retestable according to the probabilistic correctness associated with the used refinement. The potential of using FLiRTS is demonstrated on a simple case study. The results are promising and comparable to those obtained from a model-based approach (MaRTS) that requires detailed design models, and a code-based approach (DejaVu).