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Meaningful metrics for multi-level modelling
Kühne, Thomas
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Lange, Arne
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DOI:
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https://doi.org/10.1145/3417990.3421412
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URL:
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https://dl.acm.org/doi/abs/10.1145/3417990.3421412
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Additional URL:
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https://openaccess.wgtn.ac.nz/articles/conference_...
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Document Type:
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Conference or workshop publication
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Year of publication:
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2020
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Book title:
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MODELS '20 : Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, Virtual Event Canada, October 2020
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The title of a journal, publication series:
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Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings
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Page range:
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Article 85,1-9
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Conference title:
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MODELS '20
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Location of the conference venue:
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Online
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Date of the conference:
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16.-23.10.2020
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Publisher:
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Syriani, Eugene
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Sahraoui, Houari
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Place of publication:
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New York, NY
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Publishing house:
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Association for Computing Machinery
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ISBN:
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978-1-4503-7019-6
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Publication language:
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English
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Institution:
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School of Business Informatics and Mathematics > Softwaretechnik (Atkinson)
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Subject:
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004 Computer science, internet
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Abstract:
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One of the key enablers of further growth of multi-level modeling will be the development of objective ways to allow multi-level modeling approaches to be compared to one another and to two-level modeling approaches. While significant strides have been made regarding qualitative comparisons, there is currently no adequate way to quantitatively assess to what extent a multi-level model may be preferable over another model with respect to high-level qualities such as understandability, maintainability, and control capacity. In this paper, we propose deep metrics, as an approach to quantitatively measure high-level model concerns of multi-level models that are of interest to certain stakeholders. Beyond the stated goals, we see deep metrics as furthermore supporting the comparison of modeling styles and aiding modelers in making individual design decisions. We discuss what makes a metric "depth-aware" so that it can appropriately capture multi-level model properties, and present two concrete proposals for metrics that measure high-level multi-level model qualities.
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Additional information:
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Online-Ressource
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
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