Multi-amalgamated triple graph grammars

Abstract

Triple Graph Grammars (TGGs) are a well-known technique for rule-based specification of bidirectional model transformation. TGG rules build up consistent models simultaneously and are operationalized automatically to forward and backward rules describing single transformation steps in the respective direction. These operational rules, however, are of fixed size and cannot describe transformation steps whose size can only be determined at transformation time for concrete models. In particular, transforming an element to arbitrary many elements depending on the transformation context is not supported. To overcome this limitation, we propose the integration of the multi-amalgamation concept from classical graph transformation into TGGs. Multi-Amalgamation formalizes the combination of multiple transformations sharing a common subpart to a single transformation. For TGGs, this enables repeating certain parts of a forward or backward transformation step in a for each loop-like manner depending on concrete models at transformation time.