Abstract. Testing Software Product Lines is very challenging due to a high degree of variability leading to an enormous number of possible products. The vast majority of today's testing approaches for Software Product Lines validate products individually using different kinds of reuse techniques for testing. Due to the enormous number of possible products, individual product testing becomes more and more unfeasible. Combinatorial testing offers one possibility to test a subset of all possible products. In this contribution we provide a detailed description of a methodology to apply combinatorial testing to a feature model of a Software Product Line. We combine graph transformation, combinatorial testing, and forward checking for that purpose. Additionally, our approach considers predefined sets of products.
Testing Software Product Lines is a very challenging task and approaches like combinatorial testing and model-based testing are frequently used to reduce the effort of testing Software Product Lines and to reuse test artifacts. In this contribution we present a tool chain realizing our MoSo-PoLiTe concept combining combinatorial and model-based testing. Our tool chain contains a pairwise configuration selection component on the basis of a feature model. This component implements an heuristic finding a minimal subset of configurations covering 100% pairwise interaction. Additionally, our tool chain allows the model-based test case generation for each configuration within this generated subset. This tool chain is based on commercial tools since it was developed within industrial cooperations. A non-commercial implementation of pairwise configuration selection is available and an integration with an Open Source model-based testing tool is under development.
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