Model transformation in the large

Klar, Felix; Königs, Alexander; Schürr, Andy

doi:10.1145/1287624.1287664

Cited by 35 publications

(29 citation statements)

References 8 publications

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“…There are different implementations of TGGs, whereby our comparison bases on the one of MOFLON. Although MOFLON's current implementation of the execution engine of TGGs (MOFLON 1.5.1) does not yet support inheritance, TGGs were included, since specific literature concerning inheritance support exists [10]. In order to compare the bidirectional TGG-based model transformation approach with the unidirectional languages ATL and ETL, we considered only the unidirectional forward translation.…”

Section: Comparison Of Transformation Languagesmentioning

confidence: 99%

“…Inheritance support in ATL is briefly described in [9], and that in ETL in [11], but rather on a syntactical level, while the actual execution semantics are left open. A detailed discussion of semantic constraints that must be considered in TGG rule inheritance can be found in [10]. For graph transformations in general, Bardohl et.…”

Section: Related Workmentioning

confidence: 99%

“…The third dimension of the comparison framework describes how inheriting rules interact at run-time, i.e, dynamic semantics. On the basis of this framework, inheritance mechanisms in dedicated transformation languages (ATL [9], ETL [11], TGGs (MOFLON) [10]) are compared. The results show that the inheritance semantics of these languages differ, which has profound consequences for the design of transformation rules.…”

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confidence: 99%

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A Comparison of Rule Inheritance in Model-to-Model Transformation Languages

Wimmer

Kappel

Kusel

et al. 2011

Theory and Practice of Model Transformations

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Abstract. Although model transformations presumably play a major role in Model-Driven Engineering, reuse mechanisms such as inheritance have received little attention so far. In this paper, we propose a comparison framework for rule inheritance in declarative model-to-model transformation languages, and provide an in-depth evaluation of three prominent representatives thereof, namely ATL, ETL (declarative subsets thereof), and TGGs. The framework provides criteria for comparison along orthogonal dimensions, covering static aspects, which indicate whether a set of inheriting transformation rules is well-formed at compiletime, and dynamic aspects, which describe how inheriting rules behave at run-time. The application of this framework to dedicated transformation languages shows that, while providing similar syntactical inheritance concepts, they exhibit different dynamic inheritance semantics and offer basic support for checking static inheritance constraints, only.

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Section: Comparison Of Transformation Languagesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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A Comparison of Rule Inheritance in Model-to-Model Transformation Languages

Wimmer

Kappel

Kusel

et al. 2011

Theory and Practice of Model Transformations

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“…As initially presented by Klar et al [7], a viable means of addressing these challenges is to avoid pattern duplication in TGG rules by reusing rule fragments. Existing modularity concepts [7,5], however, pose strong restrictions on the way rules can be reused.…”

Section: Introductionmentioning

confidence: 99%

Modularizing Triple Graph Grammars Using Rule Refinement

Anjorin

Saller

Lochau

et al. 2014

Fundamental Approaches to Software Engineering

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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.

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“…They are not compositional in the sense that we cannot introduce intermediate models for gluing model transformations. This makes them hard to support systematic development of model transformations in the large [8]. However, composition comes at the cost of efficiency; many unnecessary intermediate models might be produced.…”

Section: Introductionmentioning

confidence: 99%

GRoundTram: An integrated framework for developing well-behaved bidirectional model transformations

et al. 2013

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Abstract-Bidirectional model transformation is useful for maintaining consistency between two models, and has many potential applications in software development including model synchronization, round-trip engineering, and software evolution. Despite these attractive uses, the lack of a practical tool support for systematic development of well-behaved and efficient bidirectional model transformation prevents it from being widely used. In this paper, we solve this problem by proposing an integrated framework called GRoundTram, which is carefully designed and implemented for compositional development of well-behaved and efficient bidirectional model transformations. GRoundTram is built upon a well-founded bidirectional framework, and is equipped with a user-friendly language for coding bidirectional model transformation, a new tool for validating both models and bidirectional model transformations, an optimization mechanism for improving efficiency, and a powerful debugging environment for testing bidirectional behavior. GRoundTram has been used by people of other groups and their results show its usefulness in practice.

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Model transformation in the large

Cited by 35 publications

References 8 publications

A Comparison of Rule Inheritance in Model-to-Model Transformation Languages

A Comparison of Rule Inheritance in Model-to-Model Transformation Languages

Modularizing Triple Graph Grammars Using Rule Refinement

GRoundTram: An integrated framework for developing well-behaved bidirectional model transformations

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