Towards Evolutionary Multi-layer Modeling with DMLA

Sándor Bácsi, Dániel Palatinszky, Máté Hidvégi

2021

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.

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