Towards statistical comparison and analysis of models

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureHoofdstukAcademicpeer review

7 Citaten (Scopus)
101 Downloads (Pure)

Samenvatting

Model comparison is an important challenge in model-driven engineering, with many application areas such as model versioning and domain model recovery. There are numerous techniques that address this challenge in the literature, ranging from graph-based to linguistic ones. Most of these involve pairwise comparison, which might work, e.g. for model versioning with a small number of models to consider. However, they mostly ignore the case where there is a large number of models to compare, such as in common domain model/metamodel recovery from multiple models. In this paper we present a generic approach for model comparison and analysis as an exploratory first step for model recovery. We propose representing models in vector space model, and applying clustering techniques to compare and analyse a large set of models. We demonstrate our approach on a synthetic dataset of models generated via genetic algorithms.
Originele taal-2Engels
TitelProceedings of the 4th International Conference on Model-Driven Engineering and Software Development, February 19-21, 2016, in Rome, Italy
Pagina's361-367
DOI's
StatusGepubliceerd - 2016
Evenement4th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2016) - Rome, Italië
Duur: 19 feb 201621 feb 2016
http://www.modelsward.org/?y=2016

Congres

Congres4th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2016)
Verkorte titelMODELSWARD 2016
LandItalië
StadRome
Periode19/02/1621/02/16
Internet adres

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Citeer dit

Babur, Ö., Cleophas, L., Verhoeff, T., & van den Brand, M. (2016). Towards statistical comparison and analysis of models. In Proceedings of the 4th International Conference on Model-Driven Engineering and Software Development, February 19-21, 2016, in Rome, Italy (blz. 361-367) https://doi.org/10.5220/0005799103610367