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.
CITATION STYLE
Babur, Ö., Cleophas, L., van den Brand, M., Tekinerdogan, B., & Aksit, M. (2018). Models, More Models, and Then a Lot More. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10748 LNCS, pp. 129–135). Springer Verlag. https://doi.org/10.1007/978-3-319-74730-9_10
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