MaRTS: A Model-Based Regression Test Selection Approach

Models can be used to plan the evolution and runtime adaptation of a software system. Regression testing of the evolved and adapted models is important to ensure that the previously tested functionality is not broken. Regression testing is performed with limited time and resource constraints. Thus, regression test selection (RTS) techniques are needed to reduce the cost of regression testing. Existing model-based RTS approaches cannot detect all types of fine-grained changes that can be made at a low level of abstraction, and they do not consider the impact of inheritance hierarchy changes on the selection of test cases. We propose a model-based RTS approach called MaRTS that classifies test cases based on changes performed to UML class and activity diagrams. It supports both fine-grained and inheritance hierarchy changes. We compared MaRTS with two code-based RTS approaches using four applications. MaRTS achieved results comparable to a dynamic code-based RTS approach (DejaVu), and outperformed a static code-based RTS approach (ChEOPSJ). The fault detection ability of the selected test cases was equal to that of the baseline test cases.