Towards Flexible, Rigorous Refinement in Metamodeling

Mezei, Gergely; Theisz, Zoltán; Bácsi, Sándor; Somogyi, Ferenc A.; Palatinszky, Dániel

doi:10.1109/models-c.2019.00073

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Properties of this kind would have to be compound in nature as they would have to, at each non-bottom-level, simultaneously have an order-zero value, and a higher-order type. We are unaware of a respective domain example and most modeling technologies do not accommodate this property kind (DMLA being a notable exception [8]). We therefore do not cover this property kind in our comparisons.…”

Section: ) Multiple Types Multiple Values (Mtmv)mentioning

confidence: 99%

“…4) support separate control over field-and value presence. Since they are based on refinement (as used in [8], for example), the elements containing level-blind fields are not organized in levels and afford much more flexibility. For example, it is possible to add or remove elements in refinement chains without invalidating existing elements (e.g., by affecting their order and thus fundamentally changing their nature).…”

Section: E Variant 5: Level-blind Fieldsmentioning

confidence: 99%

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Field Types for Deep Characterization in Multi-Level Modeling

Kühne,

Almeida,

Atkinson

et al. 2023

2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)

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Traditional two-level modeling approaches distinguish between class-and object features. Using UML parlance, classes have attributes which require their instances to have object slots. Multi-Level Modeling unifies classes and objects to "clabjects", and it has been suggested that attributes and slots can and should be unified to "fields" in a similar way. The notion of deep instantiation for clabjects creates the possibility of "deep fields", i.e., fields that expand on the roles of pure attributes or pure slots. In this paper, we discuss several variants of such a "deep field" notion, pointing out the semantic differences and the various resulting trade-offs. We hope our observations will help clarify the range of options for supporting clabject fields in multi-level modeling and thus aid future MLM development.

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Section: ) Multiple Types Multiple Values (Mtmv)mentioning

confidence: 99%

Section: E Variant 5: Level-blind Fieldsmentioning

confidence: 99%

Field Types for Deep Characterization in Multi-Level Modeling

Kühne,

Almeida,

Atkinson

et al. 2023

2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)

Self Cite

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“…This interpretation of instantiation is supported by non-strict meta-modeling, meaning that entities can refer to each other independently of their abstraction level. For the sake of clarity, it is worth mentioning that in previous work (e.g., [44], [61]), we have used two more terms to characterize the instantiation in DMLA: lazy instantiation and fluid meta-modeling. Lazy instantiation refers to the fact that it is allowed to postpone decisions regarding the instantiation of entities.…”

Section: Main Concepts Of Dmlamentioning

confidence: 99%

Playground for multi-level modeling constructs

et al. 2021

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In recent years, multi-level modeling has become more and more popular. It is mainly due to the fact that multi-level modeling aims to reduce or even totally eliminate any accidental complexity inadvertently created as by-product in traditional model design. Moreover, besides reducing model complexity, multi-level modeling also improves on general comprehension of models. The key enablers of multi-level modeling are the concepts of clabjects and deep instantiation. The latter is often governed by the potency notion, of which many different interpretations and variations emerged over the years. However, there exist also some approaches that disregard the potency notion. Thus, multi-level modeling approaches tend to take advantage of different theoretical and practical backgrounds. In this paper, we propose a unifying framework, the Multi-Level Modeling Playground (MLMP), which is a validating modeling environment for multi-level modeling research. The MLMP environment is based on our multi-layer modeling framework (the Dynamic Multi-Layer Algebra), which provides useful mechanisms to validate different multi-level modeling constructs. Since beyond the structure also the well-formedness rules of the modeling constructs can be specified, our proposed MLMP environment delivers several practical benefits: i) well-formedness is always verified, ii) multi-level constructs can be experimented with independently of any concrete tool chains, and iii) relationships (i.e., correlations or exclusions) between different multi-level constructs can be easily investigated in practice. Also, the capability of the environment is demonstrated via complete examples inspired by state-of-the-art research literature.

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The challenges of visualizing DMLA models

Hídvégi

Mezei

Bácsi

2021

Pollack

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Multi-layer metamodeling is a metamodeling method that provides great flexibility for domain prototyping, while it also ensures rigid validation of domain rules. Visualization of multi-layer models would be essential in order to promote this new modeling technique in industrial setups; however, the traditional way of representing model entities is not sufficient here. Dynamic Multi-Layer Algebra is a multi-layer metamodeling approach that addressed the challenges of multi-layer modeling, but uses a text-based language to create models. This paper presents Dynamo, a visual notation for Dynamic Multi-Layer Algebra, which is also applicable to other multi-layer metamodeling approaches. The paper elaborates the graphical syntax and the design decisions.

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Towards Flexible, Rigorous Refinement in Metamodeling

Cited by 3 publications

References 3 publications

Field Types for Deep Characterization in Multi-Level Modeling

Field Types for Deep Characterization in Multi-Level Modeling

Playground for multi-level modeling constructs

The challenges of visualizing DMLA models

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