A Variability-Based Approach to Reusable and Efficient Model Transformations

Abstract

Abstract. Large model transformation systems often contain transformation rules that are substantially similar to each other, causing performance bottlenecks for systems in which rules are applied nondeterministically, as long as one of them is applicable. We tackle this problem by introducing variability-based graph transformations. We formally define variability-based rules and contribute a novel match-finding algorithm for applying them. We prove correctness of our approach by showing its equivalence to the classic one of applying the rules individually, and demonstrate the achieved performance speed-up on a realistic transformation scenario.

Multilevel coupled model transformations for precise and reusable definition of model behaviour

“…For each perspective, the authors discuss two approaches: subtyping and mapping, and lifting and aggregating. In [75] a variability-based graph transformation approach is introduced to tackle the performance problems that are introduced by systems in which a substantial number of the rules are similar to each other. In [76] an approach to reusable model transformations is presented, which is based on sub-typing an effective part of the existing source metamodel.…”

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…”

A systematic approach to constructing families of incremental topology control algorithms using graph transformation

“…Until today, there are only few contributions that connect SPLs with GT. For instance, in [75], the authors propose to model families of GT rules by merging multiple related GT rules into one GT rule whose variables are annotated with presence conditions, which specify for each annotated variable in which variant of the GT rule it is present. Based on this approach, the authors present tool support for automatically deriving variability-based GT rules from a set of traditional GT rules [74] and for editing the derived variability-aware rules [76].…”