Block-based modeling languages, such as MATLAB/Simulink or state charts, reduce the complexity inherent to developing large-scale software systems. When creating variants for largely similar yet different software systems, the common practice is to copy models and modify them to different requirements. While this allows companies to save costs in the short-term, these so-called clone-and-own approaches cause problems regarding long-term evolution and system quality as the relation between the variants of the resulting software family is lost so that the variants have to be maintained in isolation. To recreate information regarding the variants' relations, variability mining identifies common and varying parts of cloned variants but, currently, the respective algorithms have to be created for each target language individually. In this paper, we present a generalized method to instantiate variability mining for arbitrary block-based modeling languages. The identified variability information allows developers to understand the variability of their grown software family. This knowledge helps efficiently maintaining the variants and allows migrating from clone-and-own approaches to more elaborate reuse strategies, such as software product lines. We demonstrate the feasibility of our method by instantiating variability mining techniques for two block-based languages.

In this paper we present recent developments in reverse engineering variability for block-based data-flow models.