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Models, More Models, and Then a Lot More

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationSoftware Technologies: Applications and Foundations
Subtitle of host publicationSTAF 2017 Collocated Workshops, Marburg, Germany, July 17-21, 2017, Revised Selected Papers
EditorsMartina Seidl, Steffen Zschaler
Place of PublicationCham
PublisherSpringer
Pages129-135
ISBN (Electronic)9783319747309
ISBN (Print)9783319747293
DOIs
Publication statusPublished - 23 Jan 2018
EventSTAF 2017 - Marburg, Germany
Duration: 17 Jul 201721 Jul 2017

Publication series

NameLecture notes in computer science
Volume10748
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference/symposium

Conference/symposiumSTAF 2017
Country/TerritoryGermany
CityMarburg
Period17/07/1721/07/17

Keywords

  • Data mining
  • Machine learning
  • Model analytics
  • Model-Driven Engineering
  • Scalability

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