Towards incremental model slicing for delta-oriented software product lines
Model-based behavioral specifications build the basis for comprehensive quality assurance techniques for complex software systems such as model checking and model-based testing. Various attempts exist to adopt those approaches to variant-rich applications as apparent in software product line engineering to efficiently analyze families of similar software systems. Therefore, models are usually enriched with capabilities to explicitly specify variable parts by means of annotations denoting selection conditions over feature parameters. However, a major drawback of model-based engineering is still its lack of scalability. Model slicing provides a promising technique to reduce models to only those objects being relevant for a certain criterion under consideration such as a particular test goal. Here, we present an approach for slicing feature-annotated state machine models. To support feature-oriented slicing on those models, our framework combines principles of variability encoding and conditioned slicing. We also present an implementation and provide experimental results concerning the efficiency of the slicing algorithm.