Hierarchical clustering of metamodels for comparative analysis and visualization

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    Many applications in Model-Driven Engineering involve processing multiple models or metamodels. A good example is the comparison and merging of metamodel variants into a common metamodel in domain model recovery. Although there are many sophisticated techniques to process the input dataset, little attention has been given to the initial data analysis, visualization and filtering activities. These are hard to ignore especially in the case of a large dataset, possibly with outliers and sub-groupings. In this paper we present a generic approach for metamodel comparison, analysis and visualization as an exploratory first step for domain model recovery. We propose representing metamodels in a vector space model, and applying hierarchical clustering techniques to compare and visualize them as a tree structure. We demonstrate our approach on two Ecore datasets: a collection of 50 state machine metamodels extracted from GitHub as top search results; and ∼

    100 metamodels from 16 different domains, obtained from AtlanMod Metamodel Zoo.
    Original languageEnglish
    Title of host publicationModelling Foundations and Applications
    Subtitle of host publication12th European Conference, ECMFA 2016, Held as Part of STAF 2016, Vienna, Austria, July 6-7, 2016, Proceedings
    EditorsA. Wąsowski, H. Loenn
    Place of PublicationDordrecht
    ISBN (Electronic)978-3-319-42061-5
    ISBN (Print)978-3-319-42060-8
    Publication statusPublished - 2016

    Publication series

    NameLecture Notes in Computer Science


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