Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/43037
Title: Expressive and Efficient Model Transformation with an Internal DSL of Xtend
Authors: Boronat, Artur
First Published: 14-Oct-2018
Presented at: MODELS '18, 21th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, Copenhagen, Denmark
Start Date: 14-Oct-2018
End Date: 19-Oct-2018
Publisher: ACM
Citation: MODELS '18, Proceedings of the 21th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, 2018, pp. 78-88
Abstract: Model transformation (MT) of very large models (VLMs), with millions of elements, is a challenging cornerstone for applying Model-Driven Engineering (MDE) technology in industry. Recent research efforts that tackle this problem have been directed at distributing MT on the Cloud, either directly, by managing clusters explicitly, or indirectly, via external NoSQL data stores. In this paper, we draw attention back to improving efficiency of model transformations that use EMF natively and that run on non-distributed environments, showing that substantial performance gains can still be reaped on that ground. We present Yet Another Model Transformation Language (YAMTL), a new internal domain-specific language (DSL) of Xtend for defining declarative MT, and its execution engine. The part of the DSL for defining MT is similar to ATL in terms of expressiveness, including support for advanced modelling contructs, such as multiple rule inheritance and module composition. In addition, YAMTL provides support for specifying execution control strategies. We experimentally demonstrate that the presented transformation engine outperforms other representative MT engines by using the batch transformation component of the VIATRA CPS benchmark. The improvement is, at least, one order of magnitude over the up-to-now fastest solution in all of the assessed scenarios.
DOI Link: 10.1145/3239372.3239386
ISBN: 978-1-4503-4949-9
Links: https://dl.acm.org/citation.cfm?doid=3239372.3239386
http://hdl.handle.net/2381/43037
Version: Post-print
Status: Peer-reviewed
Type: Conference Paper
Rights: Copyright © 2018, ACM. Deposited with reference to the publisher’s open access archiving policy. (http://www.rioxx.net/licenses/all-rights-reserved)
Description: The software artefacts accompanying this work have been approved by the artefact evaluation committee and are available at http://remodd.org/node/585.
Appears in Collections:Conference Papers & Presentations, Dept. of Computer Science

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