Towards Evolutionary Multi-layer Modeling with DMLA

Sándor Bácsi, Dániel Palatinszky, Máté Hidvégi

Abstract

State-of-the-art meta-model based methodologies are facing increasing pressure under new challenges originating from practical applications. In such cases, there is a strong need for approaches that support continuous, fine-graded, incremental refining of concepts. To address these challenges, our research group started working on a new modeling framework, the Dynamic Multi-Layer Algebra (DMLA) a few years ago. DMLA follows a completely new modeling paradigm, referred to as multi-layer modeling. Multi-layer modeling is originated from multi-level modeling and offers a highly flexible abstraction management approach in a level-blind fashion through its advanced deep instantiation and evolutionary snapshot management. One of the key features of DMLA is its self-validation mechanism based on a built-in, completely modeled operation language. Our initial solution had its limitations, since interactive editing was not supported, modelers could interact only with a single snapshot of the model. To overcome the limitations, we have created a virtual machine and an interpreter. In this paper, we present the novel architecture of our solution and demonstrate the feasibility of our approach by a walk-through of the concrete model management steps of an illustrative example to show the benefits of evolutionary model editing in DMLA.

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Paper Citation


in Harvard Style

Bácsi S., Palatinszky D. and Hidvégi M. (2021). Towards Evolutionary Multi-layer Modeling with DMLA.In Proceedings of the 9th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD, ISBN 978-989-758-487-9, pages 344-349. DOI: 10.5220/0010347403440349


in Bibtex Style

@conference{modelsward21,
author={Sándor Bácsi and Dániel Palatinszky and Máté Hidvégi},
title={Towards Evolutionary Multi-layer Modeling with DMLA},
booktitle={Proceedings of the 9th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD,},
year={2021},
pages={344-349},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010347403440349},
isbn={978-989-758-487-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD,
TI - Towards Evolutionary Multi-layer Modeling with DMLA
SN - 978-989-758-487-9
AU - Bácsi S.
AU - Palatinszky D.
AU - Hidvégi M.
PY - 2021
SP - 344
EP - 349
DO - 10.5220/0010347403440349