Abstracts Track 2021

Area 1 - Modeling Languages, Tools and Architectures

Nr: 9

Towards Model Transformation with Level-spanning Patterns


Sándor Bácsi and Gergely Mezei

Abstract: As model-driven engineering (MDE) aims at solving real-world challenges it needs to support increasing industrial demands. In the age of Industry 4.0, the domains tend to be much more complex, heterogeneous and diversified than a decade ago. Highly abstract and concrete concepts are to be connected and handled as a whole. Classical two (or four)-level modeling approaches struggle to deal with such setups without introducing accidental complexity. Multi-level modeling is a relatively new software development paradigm aimed at tackling such complex domains that would otherwise be difficult to express. In MDE, models are meant to be transformed, thus model transformation is one of the basic pillars of MDE. Besides generating source code, transformation is often used to refactor or optimize the models. While the theory and application of graph transformations are active research fields for many years now, existing approaches focus mainly on classic meta-modeling setups. There are many efficient algorithms to find and replace patterns in meta-models, but these algorithms suppose that all elements of the pattern are on the same abstraction level. It is a promising direction however to use graph pattern-based transformations on multi-level models as well. Our research aims at this goal: creating an approach that supports graph transformation consisting of level-spanning patterns. These patterns could capture domain concepts across multiple abstraction levels, where the elements can refer to each other independently of their abstraction level. A typical modeling scenario for this is when we have a domain concept containing several components, some of which are concrete, while others are more abstract, i.e. not yet specified completely. In this work, we propose reusable model transformation patterns which can capture level-spanning domain concepts in a multi-level context. The feasibility of the model transformation patterns is also demonstrated in our multi-layer modeling framework, the Dynamic Multi-Layer Algebra (DMLA) by the manipulation of concrete domain models.