Feature interaction testing of variability intensive systems

Testing variability intensive systems is a formidable task due to the combinatorial explosion of feature interactions that result from all variations. We developed and validated an approach of combinatorial test generation using Multi-Perspective Feature Models (MPFM). MPFMs are a set of feature models created to achieve Separation of Concerns within the model. This approach improves test coverage of variability. 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. We discuss the learning from this experiment and further research potential in testing variability intensive systems.