Combinatorial testing for feature models using CitLab

Feature models are commonly used to represent product lines and systems with a set of features interrelated each others. Test generation from feature models, i.e. generating a valid and representative subset of all the possible product configurations, is still an open challenge. A common approach is to build combinatorial interaction test suites, for instance achieving pairwise coverage among the features. In this paper we show how standard feature models can be translated to combinatorial interaction models in our framework CITLAB, with all the advantages of having a combinatorial testing environment (in terms of a clear semantics, editing facilities, language for seeds and test goals, and generation algorithms). We present our translation which gives a precise semantics to feature models and it tries to minimize the number of parameter and constraints while preserving the original semantics of the feature model. Experiments show the advantages of our approach.