Enhancing expressivity, modularity and rigour of graphical data modelling with Fragmenta

Amálio, N ORCID: https://orcid.org/0000-0001-8751-5039, 2025. Enhancing expressivity, modularity and rigour of graphical data modelling with Fragmenta. ACM Transactions on Software Engineering and Methodology. ISSN 1049-331X

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Abstract

Graphical data (or conceptual) modelling, software engineering's most popular application of modelling, needs an uplift. Graphical data models easily become unwieldy, even for trivial medium-sized systems. Practitioners long for improved modelling primitives and mechanisms that improve modelling, namely, its expressiveness, the way it is experienced and its appeal. These are the drivers of Fragmenta, a formal data- and meta-modelling framework with advanced means to separate concerns. Fragmenta's novelties include: (i) multilevel modelling through vertical refinement, (ii) improved horizontal decomposition and composition of fragments through proxies, (iii) virtuals for lighter inheritance, and (iv) graphical model constraints. Fragmenta is the first data modelling framework with a mathematical foundation and a supporting implementation, combining both vertical and horizontal means to separate concerns.

Item Type:	Journal article
Publication Title:	ACM Transactions on Software Engineering and Methodology
Creators:	Amálio, N.
Publisher:	Association for Computing Machinery (ACM)
Date:	4 September 2025
ISSN:	1049-331X
Identifiers:	Number Type 10.1145/3765738 DOI 2575396 Other
Rights:	© ACM 2026. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Software Engineering and Methodology, http://dx.doi.org/10.1145/3765738
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Borcherds
Date Added:	10 Mar 2026 08:32
Last Modified:	10 Mar 2026 08:32
URI:	https://irep.ntu.ac.uk/id/eprint/55378

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