Models, More Models, and Then a Lot More

With increased adoption of Model-Driven Engineering, the number of related artefacts in use, such as models, metamodels and transformations, greatly increases. To confirm this, we present quantitative evidence from both academia - in terms of repositories and datasets - and industry - in terms of large domain-specific language ecosystems. To be able to tackle this dimension of scalability in MDE, we propose to treat the artefacts as data, and apply various techniques - ranging from information retrieval to machine learning - to analyse and manage those artefacts in a holistic, scalable and efficient way.