Towards Statistical Comparison and Analysis of Models

Önder Babur, Önder Babur, Loek Cleophas, Loek Cleophas, Tom Verhoeff, Tom Verhoeff, Mark van den Brand, Mark van den Brand


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.


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Paper Citation

in Harvard Style

Babur Ö., Cleophas L., Verhoeff T. and 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 - Volume 1: MODELSWARD, ISBN 978-989-758-168-7, pages 361-367. DOI: 10.5220/0005799103610367

in Bibtex Style

author={Önder Babur and Loek Cleophas and Tom Verhoeff and Mark van den Brand},
title={Towards Statistical Comparison and Analysis of Models},
booktitle={Proceedings of the 4th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD,},

in EndNote Style

JO - Proceedings of the 4th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD,
TI - Towards Statistical Comparison and Analysis of Models
SN - 978-989-758-168-7
AU - Babur Ö.
AU - Cleophas L.
AU - Verhoeff T.
AU - van den Brand M.
PY - 2016
SP - 361
EP - 367
DO - 10.5220/0005799103610367