Modularizing Triple Graph Grammars Using Rule Refinement

Abstract

Abstract. Model transformation plays a central role in Model-Driven Engineering. In application scenarios such as tool integration or view specification, bidirectionality is a crucial requirement. Triple Graph Grammars (TGGs) are a formally founded, bidirectional transformation language, which has been used successfully in various case studies from different applications domains.In practice, supporting the maintainability of TGGs is a current challenge and existing modularity concepts, e.g., to avoid pattern duplication in TGG rules, are still inadequate. Existing TGG tools either provide no support at all for modularity, or provide limited support with restrictions that are often not applicable.In this paper, we present and formalize a novel modularity concept for TGGs: Rule refinement, which generalizes existing modularity concepts, solves the problem of pattern duplication, and enables concise, maintainable specifications.

Taming Multi-Variability of Software Product Line Transformations

“…In the domain of graph transformation reuse, rule refinement [9] and amalgamation [38] focus on reuse at the rule level; graph variability is not in their scope. Rensink and Ghamarian propose a solution for rule and graph decomposition based a certain accommodation condition, under which the effect of the original rule application is preserved [39,40].…”

“…Yet, in complex transformation scenarios as increasingly found in practice [7], not only the considered models include variations: The transformation system can contain variability as well, for example, due to desired optional behavior of rules, or for rule variants arising from the sheer complexity of the involved meta-models. While a number of works [8][9][10] support systematic reuse to improve maintainability, variability-based model transformation (VB) [11,12] also aims to improve the performance when a transformation system with many similar rules is executed. To this end, these rules are represented as a single rule with variability annotations, called VB rule.…”

Model clone detection for rule-based model transformation languages

“…While the strategies used in specic refactorings may vary, they share the common requirement that a target reuse mechanism is assumed. In the case of model transformations, reuse approaches such as rule inheritance [18], renement [19] or variability-based rules [20] have emerged recently and are now available to developers. For instance, the rules in…”

“…Micro-clones can be avoided by specifying multiplicity at the level of individual graph nodes and edges [34]. Internal clones can be replaced using reuse mechanisms such as rule inheritance [18], renement [19], or variability-based rules [20]. A suitable alternative to the creation of external clones are external reuse approaches, such as generic model transformations [35].…”

A Variability-Based Approach to Reusable and Efficient Model Transformations

“…In such languages, a base rule is refined by a set of sub-rules modifying it. Then, some approaches [15,16] flatten the rules for application, i.e., compile them into simpler rules. The translational semantics in the approach proposed in RubyTL [17] is closest to ours -it applies the base rules first and then applies the refinement rules on the target model of the transformation.…”