Information Preserving Bidirectional Model Transformations

Abstract

Abstract.Within model-driven software development, model transformation has become a key activity. It refers to a variety of operations modifying a model for various purposes such as analysis, optimization, and code generation. Most of these transformations need to be bidirectional to e.g. report analysis results, or keep coherence between models. In several application-oriented papers it has been shown that triple graph grammars are a promising approach to bidirectional model transformations. But up to now, there is no formal result showing under which condition corresponding forward and backward transformations are inverse to each other in the sense of information preservation. This problem is solved in this paper based on general results for the theory of algebraic graph transformations. The results are illustrated by a transformation of class models to relational data base models which has become a quasistandard example for model transformation.

Triple Graph Grammars in the Large for Translating Satellite Procedures

“…In the following, we briefly review main concepts for model transformation based on TGGs [10]. A triple graph is an integrated model consisting of a source model, a target model and explicit correspondences between them.…”

“…4 commute). Triple graphs are typed over a triple type graph TG and attributed according to [10]. For a triple type graph…”

Pattern-Based Model-to-Model Transformation: Handling Attribute Conditions

“…Nodes in the correspondence graph relate nodes in the source and target graphs by means of two graph morphisms [5], and for technical reasons we restrict them to be unattributed (i.e. D C = ∅).…”

“…We also generate TGG operational rules, but whereas declarative TGG rules must say which elements should exist and which ones are created, our heuristics infer such information. Moreover, TGGs need a control mechanism to guide the execution of the operational rules, such as priorities [8] or their coupling to editing rules [5], while our patterns do not need it. As in QVT, there is no equivalent to our N-patterns, however TGGs can be seen as a subset of our approach, where a declarative TGG rule is a pattern of the form ← − P (L) ⇒ P (R).…”

“…Patterns can specify positive information (the relation they declare must hold) or negative one (the relation must not hold). A pattern specification is compiled into operational mechanisms, implemented with Triple Graph Grammar (TGG) operational rules [5,8,14], to perform forward and backward transformations.…”