Combinatorial Interaction Testing with Multi-perspective Feature Models

Testing product lines and similar software involves the important task of testing feature interactions. The challenge is to test all those feature interactions that result in testing of all variations across all dimensions of variation. In this context, we propose the use of combinatorial test generation, with Multi-Perspective Feature Models (MPFM) as the input model. MPFMs are a set of feature models created to achieve Separation of Concerns within the model. We believe that the MPFM is useful as an input model for combinatorial testing and it is easy to create and understand. This approach helps achieve a better coverage of variability in the product line. Results from an experiment on a real-life case show that up to 37% of the test effort could be reduced and up to 79% defects from the live system could be detected.