An Approach to Flexible Multilevel Modelling

Multilevel modelling approaches tackle issues related to lack of flexibility and mixed levels of abstraction by providing features like deep modelling and linguistic extension. However, the lack of a clear consensus on fundamental concepts of the paradigm has in turn led to lack of common focus in current multilevel modelling tools and their adoption. In this paper, we propose a formal framework, together with its corresponding tools, to tackle these challenges. The approach facilitates definition of flexible multilevel modelling hierarchies by allowing addition and deletion of intermediate abstraction levels in the hierarchies. Moreover, it facilitates separation of concerns by allowing integration of different multilevel modelling hierarchies as different aspects of the system to be modelled. In addition, our approach facilitates reusability of concepts and their behaviour by allowing definition of flexible transformation rules which are applicable to different hierarchies with a variable number of levels. As a proof of concept, a prototype tool and a domain-specific language for the definition of these rules is provided.publishedVersio

A General Methodology for Internalising Multi-level Model Typing

Multilevel Modelling approaches allow for an arbitrary number of abstraction levels in typing chains. In this paper, a transformation of a multi-level typing chain into a single all-covering representing model is proposed. This comprehensive model is of equal size as the most concrete model in the chain and encodes all typing information in its labels, such that the typing chain can completely be restored. This guideline for maintaining multi-level typing chains in respective implementations of multi-level typing environments is based on a categorical equivalence theorem, which we generalize to a more convenient graph-oriented version.acceptedVersio

Configurable Software Performance Completions through Higher-Order Model Transformations

Chillies is a novel approach for variable model transformations closing the gap between abstract architecture models, used for performance prediction, and required low-level details. We enable variability of transformations using chain of generators based on the Higher-Order Transformation (HOT). HOTs target different goals, such as template instantiation or transformation composition. In addition, we discuss state-dependent behavior in prediction models and quality of model transformations