Author:
Önder Babur
Affiliation:
Eindhoven University of Technology, Netherlands
Keyword(s):
Model-driven Engineering, Model Clone Detection, R, Vector Space Model, Clustering.
Related
Ontology
Subjects/Areas/Topics:
Domain-Specific Modeling and Domain-Specific Languages
;
Languages, Tools and Architectures
;
MetaModeling
;
Model-Driven Software Development
;
Models
;
Paradigm Trends
;
Reasoning about Models
;
Software Engineering
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.