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Mining metrics for understanding metamodel characteristics

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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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
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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Publication History

Published: 02 June 2014

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Author Tags

  1. Model Driven Engineering
  2. metamodel metrics
  3. metamodeling

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Overall Acceptance Rate 13 of 30 submissions, 43%

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Cited By

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  • (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
  • (2023)Language usage analysis for EMF metamodels on GitHubEmpirical Software Engineering10.1007/s10664-023-10368-x29:1Online publication date: 13-Dec-2023
  • (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
  • (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
  • (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
  • (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
  • (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
  • (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
  • (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
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