Article

Mining metrics for understanding metamodel characteristics

Authors:

Davide Di Ruscio,

Ludovico Iovino,

Alfonso PierantonioAuthors Info & Claims

MiSE 2014: Proceedings of the 6th International Workshop on Modeling in Software Engineering

Pages 55 - 60

https://doi.org/10.1145/2593770.2593774

Published: 02 June 2014 Publication History

Abstract

Metamodels are a key concept in Model-Driven Engineering. Any artifact in a modeling ecosystem has to be defined in accordance to a metamodel prescribing its main qualities. Hence, understanding common characteristics of metamodels, how they evolve over time, and what is the impact of metamodel changes throughout the modeling ecosystem is of great relevance. Similarly to software, metrics can be used to obtain objective, transparent, and reproducible measurements on metamodels too. In this paper, we present an approach to understand structural characteristics of metamodels. A number of metrics are used to quantify and measure metamodels and cross-link different aspects in order to provide additional information about how metamodel characteristics are related. The approach is applied on repositories consisting of more than 450 metamodels.

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Cited By

Cesal FBork D(2024)Establishing interoperability between EMF and MSDKVS: an M3-level-bridge to transform metamodels and modelsSoftware and Systems Modeling10.1007/s10270-024-01169-x23:4(865-894)Online publication date: 30-Apr-2024
https://doi.org/10.1007/s10270-024-01169-x
Babur ÖConstantinou ESerebrenik A(2023)Language usage analysis for EMF metamodels on GitHubEmpirical Software Engineering10.1007/s10664-023-10368-x29:1Online publication date: 13-Dec-2023
https://doi.org/10.1007/s10664-023-10368-x
De Sanctis MDi Salle AIovino LRossi M(2023)A technology transfer journey to a model-driven access control systemInternational Journal on Software Tools for Technology Transfer10.1007/s10009-023-00697-z25:1(49-74)Online publication date: 10-Feb-2023
https://doi.org/10.1007/s10009-023-00697-z
Show More Cited By

Index Terms

Mining metrics for understanding metamodel characteristics
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
2. General and reference
  1. Cross-computing tools and techniques
    1. Metrics

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cover image ACM Conferences

MiSE 2014: Proceedings of the 6th International Workshop on Modeling in Software Engineering

June 2014

64 pages

ISBN:9781450328494

DOI:10.1145/2593770

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 June 2014

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ICSE '14

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ICSE '14: 36th International Conference on Software Engineering

June 2 - 3, 2014

Hyderabad, India

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Overall Acceptance Rate 13 of 30 submissions, 43%

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Cited By

Cesal FBork D(2024)Establishing interoperability between EMF and MSDKVS: an M3-level-bridge to transform metamodels and modelsSoftware and Systems Modeling10.1007/s10270-024-01169-x23:4(865-894)Online publication date: 30-Apr-2024
https://doi.org/10.1007/s10270-024-01169-x
Babur ÖConstantinou ESerebrenik A(2023)Language usage analysis for EMF metamodels on GitHubEmpirical Software Engineering10.1007/s10664-023-10368-x29:1Online publication date: 13-Dec-2023
https://doi.org/10.1007/s10664-023-10368-x
De Sanctis MDi Salle AIovino LRossi M(2023)A technology transfer journey to a model-driven access control systemInternational Journal on Software Tools for Technology Transfer10.1007/s10009-023-00697-z25:1(49-74)Online publication date: 10-Feb-2023
https://doi.org/10.1007/s10009-023-00697-z
López JIzquierdo JCuadrado JKühn TSousa V(2022)Using the ModelSet dataset to support machine learning in model-driven engineeringProceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings10.1145/3550356.3559096(66-70)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1145/3550356.3559096
Di Rocco JDi Ruscio DDi Sipio CNguyen PPierantonio A(2022)MemoRec: a recommender system for assisting modelers in specifying metamodelsSoftware and Systems Modeling10.1007/s10270-022-00994-222:1(203-223)Online publication date: 29-Mar-2022
https://doi.org/10.1007/s10270-022-00994-2
Cesal FBork D(2022)Establishing Interoperability Between the EMF and the MSDKVS Metamodeling PlatformsThe Practice of Enterprise Modeling10.1007/978-3-031-21488-2_11(167-182)Online publication date: 17-Nov-2022
https://doi.org/10.1007/978-3-031-21488-2_11
Bork DAnagnostou KWimmer M(2022)Towards Interoperable Metamodeling Platforms: The Case of Bridging ADOxx and EMFAdvanced Information Systems Engineering10.1007/978-3-031-07472-1_28(479-497)Online publication date: 3-Jun-2022
https://doi.org/10.1007/978-3-031-07472-1_28
Rubei RRocco JRuscio DNguyen PPierantonio A(2021)A Lightweight Approach for the Automated Classification and Clustering of Metamodels2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)10.1109/MODELS-C53483.2021.00074(477-482)Online publication date: Oct-2021
https://doi.org/10.1109/MODELS-C53483.2021.00074
López JCuadrado J(2021)An efficient and scalable search engine for modelsSoftware and Systems Modeling10.1007/s10270-021-00960-421:5(1715-1737)Online publication date: 27-Dec-2021
https://doi.org/10.1007/s10270-021-00960-4
Kudo TBulcão-Neto RVincenzi ACavalcante EDantas FBatista T(2020)Toward a Metamodel Quality Evaluation FrameworkProceedings of the XXXIV Brazilian Symposium on Software Engineering10.1145/3422392.3422461(102-107)Online publication date: 21-Oct-2020
https://dl.acm.org/doi/10.1145/3422392.3422461
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