A feature-based classification of triple graph grammar variants

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. In this paper, we provide an overview of the most common language features of TGGs. Based on this, we discuss different TGG variants formally and develop a classification of TGG approaches with respect to their expressiveness. We evaluate whether certain language features increase the expressiveness of TGGs or just improve the usability and simplify the specification, which can be important when choosing a software tool depending on the concrete problem at hand. Additionally, examples implemented in the TGG tool eMoflon::IBeX are discussed, which particularly illustrate how the various TGG variants differ in their expressiveness.