Automatic variation-point identification in function-block-based models

Abstract

Function-block-based modeling is often used to develop embedded systems, particularly as system variants can be developed rapidly from existing modules. Generative approaches can simplify the handling and development of the resulting high variety of function-block-based models. But they often require the development of new generic models that do not utilize existing ones. Reusing existing models will significantly decrease the effort to apply generative programming. This work introduces an automatic approach to recognize variants in a set of models and identify the variation points and their dependencies within variants. As result it offers automatically generated feature models and ICCL content to regenerate the given variants.