A feature-based classification of triple graph grammar variants

Abstract

Bidirectional model transformations are a way to keep two models synchronized and propagate changes in one model to the other one. Triple Graph Grammars (TGGs) are a rule-based approach to define consistency bidirectionally, with applications e.g. in the development of textual and visual languages. Although the underlying formalism is relatively uniform in different TGG tools, there are various TGG variants supporting different sets of language features, such as attribute conditions, (negative) application conditions, and multi-amalgamation. This makes it difficult to evaluate the expressiveness of a specific TGG tool, to check whether the tool supports all features required to specify a given consistency relation.