Hierarchical Clustering of Metamodels for Comparative Analysis and Visualization

Babur, Önder; Cleophas, Loek; Brand, Mark van den

doi:10.1007/978-3-319-42061-5_1

Cited by 22 publications

(18 citation statements)

References 20 publications

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“…In this section, we present an evaluation of our approach. We address the following research question: 14 3 2 10 14 3 2 10 14 3 4 9 11 3 4 9 11 3 4 9 11 3 trE10 2 9 11 2 2 9 11 2 2 9 11 2 4 8 8 2 4 8 8 2 4 8 8 [46], which has been used as a grouping mechanism in the MDE context before [47]. We congured the clustering algorithm to use the average linkage strategy and clone size as the similarity metric.…”

Section: Discussionmentioning

confidence: 99%

Model clone detection for rule-based model transformation languages

2017

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Section: Discussionmentioning

confidence: 99%

Model clone detection for rule-based model transformation languages

2017

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“…The metamodels have been retrieved mainly from GitHub, and AtlanMod Zoo. 1 We have included also additional metamodels that have been developed by the authors for teaching activities and for the development of internal tools.…”

Section: Proposed Toolchainmentioning

confidence: 99%

“…feature model repository. The same authors [1] presented a clustering approach applied on two Ecore model datasets: a collection of 50 state machine metamodels extracted from GitHub and 100 metamodels obtained from AtlanMod Metamodel Zoo. Another interesting dataset has been presented [9], where an evaluation of the presented UML mining methodology has been conducted on a UML models dataset, comprising 65 Use Case diagrams and 72 Activity diagrams.…”

Section: Related Workmentioning

confidence: 99%

An extensible tool-chain for analyzing datasets of metamodels

Barriga

Ruscio

Iovino

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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Metamodels play a crucial role in any modeling environment as they formalize the modeling constructs underpinning the definition of conforming artifacts, including models, model transformations, code generators, and editors. Understanding the structural characteristics and the quality of the metamodels that are available in public repositories before their reuse is a critical task that demands the adoption of different tools, which might not be easy to adopt. Even the selection of metamodels to be used for experimenting with new tools is not straightforward as it involves exploring various sources of information and dig in each metamodel to check its appropriateness for the evaluation of the tool under development. In this paper, we present a dataset of metamodels, which has been collected for experimenting with different approaches conceived by the authors. The dataset has been automatically curated using a toolchain, which has been redesigned post-ante the definition of the proposed approaches to foster its future reuse. CCS CONCEPTS • Software and its engineering → Model-driven software engineering; Software system models; Software system structures; Software organization and properties.

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“…by Bislimovska et al [6]). Further IRbased techniques (though mainly developed for model comparison/clustering) can be found in [7], [8] involving repository management and model searching scenarios.…”

Section: Treating Mde Artefacts As Datamentioning

confidence: 99%

“…More research is needed on (1) finding the right chain of NLP tools applicable for models (in contrast with source code and documentation) and (2) reporting accuracies and disagreements between various tools (along the lines of the recent report in [9] for repository mining). c) Data Mining: Following the perspective of approaching MDE artefacts as data, we need scalable techniques to extract relevant units of information from models (features in data mining (DM) jargon), and to discover patterns including domain clusters, outliers/noise and clones (see example applications in [7], [8], [10]). To be able to analyse, explore and eventually make sense of the large datasets in MDE (e.g.…”

Section: Treating Mde Artefacts As Datamentioning

confidence: 99%