Clone Detection for Ecore Metamodels using N-grams

Önder Babur

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.

Download


Paper Citation


in Harvard Style

Babur Ö. (2018). Clone Detection for Ecore Metamodels using N-grams.In Proceedings of the 6th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD, ISBN 978-989-758-283-7, pages 411-419. DOI: 10.5220/0006604604110419


in Bibtex Style

@conference{modelsward18,
author={Önder Babur},
title={Clone Detection for Ecore Metamodels using N-grams},
booktitle={Proceedings of the 6th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD,},
year={2018},
pages={411-419},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006604604110419},
isbn={978-989-758-283-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 6th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD,
TI - Clone Detection for Ecore Metamodels using N-grams
SN - 978-989-758-283-7
AU - Babur Ö.
PY - 2018
SP - 411
EP - 419
DO - 10.5220/0006604604110419