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Systems tend to become more and more complex. This has a direct impact on system engineering processes. Two of the most important phases in these processes are requirements engineering and quality assurance. Two significant complexity drivers located in these phases are the growing number of product variants that have to be integrated into the requirements engineering and the ever growing effort for manual test design. There are modeling techniques to deal with both complexity drivers like, e.g., feature modeling and model-based test design. Their combination, however, has been seldom the focus of investigation. In this paper, we present two approaches to combine feature modeling and model-based testing as an efficient quality assurance technique for product lines. We present the corresponding difficulties and approaches to overcome them. All explanations are supported by an example of an online shop product line

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UML state machines are widely used as test models in model-based testing. Coverage criteria are applied to them, e.g. to measure a test suite's coverage of the state machine or to steer automatic test suite generation based on the state machine. The model elements to cover as described by the applied coverage criterion depend on the structure of the state machine. Model transformations can be used to change this structure. In this paper, we present semantic-preserving state machine transformations that are used to influence the result of the applied coverage criteria. The contribution is that almost every feasible coverage criterion that is applied to the transformed state machine can have at least the same effect as any other feasible, possibly stronger coverage criterion that is applied to the original state machine. We introduce simulated satisfaction as a corresponding relation between coverage criteria. We provide formal definitions for coverage criteria and use them to prove the correctness of the model transformations that substantiate the simulated satisfaction relations. The results of this paper are especially important for model-based test generation tools, which are often limited to satisfy a restricted set of coverage criteria.

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