A Comparison of Two-Level and Multi-level Modelling for Cloud-Based Applications

Rossini, Alessandro; Lara, Juan de; Guerra, Esther; Nikolov, Nikolay

doi:10.1007/978-3-319-21151-0_2

Cited by 13 publications

(9 citation statements)

References 18 publications

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“…The MLM version of the model has 50 modeling elements while the TLM version, using standard workaround patterns such as the Type-Object pattern [10], has 95 modeling elements, amounting to an increase of 90%. Rossini et al performed a similar evaluation which yielded a three-fold increase in the number of modeling elements in a two-level versus a multi-level model of their CloudML scenario [16].…”

Section: Assessing Internal Qualitiesmentioning

confidence: 99%

On evaluating multi-level modeling

Atkinson¹,

Kuehne²

2020

Preprint

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Multi-Level Modeling is receiving increasing levels of interest and its active research community is continuing to make progress. However, to advance the discipline effectively it is necessary to increase industry adoption and achieve better community cohesion. We believe that the key to addressing both these challenges is to promote the creation of more comparisons in the multi-level modeling field based on meaningful objective evaluations. In this position paper, we provide our view on what constitutes meaningful evaluations and discuss some of the issues involved in obtaining them, while presenting a broad overview of existing multi-level modeling evaluations. In particular, we emphasize the importance of understanding and managing the difference between internal and external qualities.

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Section: Assessing Internal Qualitiesmentioning

confidence: 99%

On evaluating multi-level modeling

Atkinson¹,

Kuehne²

2020

Preprint

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“…Rossini et al [31] compared a two-level version of a cloud modeling language called CloudML, which employs the type-object pattern [24], to a multi-level modeling version. The authors also assume that a small model size is desirable because a larger model is harder to comprehend.…”

Section: Related Workmentioning

confidence: 99%

Meaningful metrics for multi-level modelling

Kühne

Lange

2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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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. CCS CONCEPTS • Software and its engineering → Software design engineering; • Computing methodologies → Modeling methodologies.

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“…The complete formalization as well as other MLM examples and an evaluation of MultEcore are depicted in [Mac19]. Further examples of multilevel models with four or more levels can be checked out in [RDLGN15].…”

Section: Dsml Structure -Multilevel Modellingmentioning

confidence: 99%

Executing Multilevel Domain-Specific Models in Maude.

Rodrı́guez¹,

Durán²,

Rutle³

et al. 2019

JOT

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Multilevel modelling (MLM) tackles the limitation in the number of abstraction levels present in traditional modelling approaches within the model-driven software engineering (MDSE) field. One way to specify the behaviour description of MLMs is by means of multilevel model transformations. In this paper, we propose an approach to achieve reusability and flexibility in specifying and executing multilevel model transformations. For this purpose, we rely on code-generation and the efficient rewriting logic mechanisms that Maude provides. As a proof of concept, we have developed an infrastructure which combines our MLM tool MultEcore, that facilitates definition of MLM hierarchies and transformations, with Maude, which performs the execution of the transformations on these hierarchies.

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A Comparison of Two-Level and Multi-level Modelling for Cloud-Based Applications

Cited by 13 publications

References 18 publications

On evaluating multi-level modeling

On evaluating multi-level modeling

Meaningful metrics for multi-level modelling

Executing Multilevel Domain-Specific Models in Maude.

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