SESep 8, 2014

Metamodelling: State of the Art and Research Challenges

arXiv:1409.2359v194 citations
Originality Synthesis-oriented
AI Analysis

This is an incremental review and challenge identification for metamodelling, relevant to researchers and practitioners in model-driven engineering.

The paper reviews current approaches, abstractions, and tools for metamodelling, evaluating their expressivity and roles at run-time, and identifies research challenges related to complexity, consistency, and evolution of metamodels.

This chapter discusses the current state of the art, and emerging research challenges, for metamodelling. In the state-of-the-art review on metamodelling, we review approaches, abstractions, and tools for metamodelling, evaluate them with respect to their expressivity, investigate what role(s) metamodels may play at run-time and how semantics can be assigned to metamodels and the domain specific modeling languages they could define. In the emerging challenges section on metamodelling we highlight research issues regarding the management of complexity, consistency, and evolution of metamodels, and how the semantics of metamodels impacts each of these.

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