Clone detection for ecore metamodels using N-grams: 6th International Conference on Model-Driven Engineering and Software Development, MODELSWARD 2018

Önder Babur, Slimane Hammoudi, Luis Ferreira Pires, Bran Selic

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademic

12 Citations (Scopus)

Abstract

Increasing model-driven engineering use leads to an abundance of models and metamodels in academic and industrial practice. A key technique for the management and maintenance of those artefacts is model clone detection, where highly similar (meta-)models and (meta-)model fragments are mined from a possibly large amount of data. In this paper we extend the SAMOS framework (Statistical Analysis of MOdelS) to clone detection on Ecore metamodels, using the framework’s n-gram feature extraction, vector space model and clustering capabilities. We perform a case analysis on Ecore metamodels obtained by applying an exhaustive set of single mutations to assess the precision/sensitivity of our technique with respect to various types of mutations. Using mutation analysis, we also briefly evaluate MACH, a comparable UML clone detection tool.
Original languageEnglish
Title of host publication Proceedings of the 6th International Conference on Model-Driven Engineering and Software Development
PublisherSCITEPRESS-Science and Technology Publications, Lda.
Number of pages9
ISBN (Print)9789897582837
DOIs
Publication statusPublished - 1 Jan 2018
Externally publishedYes
EventProceedings of the 6th International Conference on Model-Driven Engineering and Software Development - Madeira, Portugal
Duration: 22 Jan 201824 Jan 2018

Conference/symposium

Conference/symposiumProceedings of the 6th International Conference on Model-Driven Engineering and Software Development
Country/TerritoryPortugal
CityMadeira
Period22/01/1824/01/18

Keywords

  • Clustering
  • Model Clone Detection
  • Model-driven Engineering
  • R
  • Vector Space Model

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