Automated Variability Injection for Graphical Modelling Languages

Antonio Garmendia; Manuel Wimmer; Esther Guerra; Elena Gómez-Martinéz; Juan de Lara

doi:10.1145/3425898.3426957

Automated Variability Injection for Graphical Modelling Languages

Antonio Garmendia, Manuel Wimmer, Esther Guerra, Elena Gómez-Martinéz, Juan de Lara

Institute of Business Informatics - Software Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

Model-based development approaches, such as Model-Driven Engineering (MDE), heavily rely on the use of modelling languages to achieve and automate software development tasks. To enable the definition of model variants (e.g., supporting the compact description of system families), one solution is to combine MDE with Software Product Lines. However, this is technically costly as it requires adapting many MDE artefacts associated to the modelling language - especially the meta-models and graphical environments. To alleviate this situation, we propose a method for the automated injection of variability into graphical modelling languages. Given the meta-model and graphical environment of a particular language, our approach permits configuring the allowed model variability, and the graphical environment is automatically adapted to enable creating models with variability. Our solution is implemented atop the Eclipse Modeling Framework and Sirius, and synthesizes adapted graphical editors integrated with Feature IDE.

Original language	English
Title of host publication	19th ACM SIGPLAN International Conference on Generative Programming, Concepts and Experiences (GPCE 2020), November 16–17, 2020, Chicago, USA, virtual
Editors	Martin Erwig, Jeff Gray
Pages	15-21
Number of pages	7
ISBN (Electronic)	9781450381741
DOIs	https://doi.org/10.1145/3425898.3426957
Publication status	Published - 16 Nov 2020

Fields of science

202017 Embedded systems
102002 Augmented reality
102006 Computer supported cooperative work (CSCW)
102015 Information systems
102020 Medical informatics
102022 Software development
102034 Cyber-physical systems
201132 Computational engineering
201305 Traffic engineering
207409 Navigation systems
502032 Quality management
502050 Business informatics
503015 Subject didactics of technical sciences

JKU Focus areas

Digital Transformation

Access to Document

10.1145/3425898.3426957

Cite this

Garmendia, A., Wimmer, M., Guerra, E., Gómez-Martinéz, E., & de Lara, J. (2020). Automated Variability Injection for Graphical Modelling Languages. In M. Erwig, & J. Gray (Eds.), 19th ACM SIGPLAN International Conference on Generative Programming, Concepts and Experiences (GPCE 2020), November 16–17, 2020, Chicago, USA, virtual (pp. 15-21) https://doi.org/10.1145/3425898.3426957

@inproceedings{13066430920740e898dc4814a4bb2620,

title = "Automated Variability Injection for Graphical Modelling Languages",

abstract = "Model-based development approaches, such as Model-Driven Engineering (MDE), heavily rely on the use of modelling languages to achieve and automate software development tasks. To enable the definition of model variants (e.g., supporting the compact description of system families), one solution is to combine MDE with Software Product Lines. However, this is technically costly as it requires adapting many MDE artefacts associated to the modelling language - especially the meta-models and graphical environments. To alleviate this situation, we propose a method for the automated injection of variability into graphical modelling languages. Given the meta-model and graphical environment of a particular language, our approach permits configuring the allowed model variability, and the graphical environment is automatically adapted to enable creating models with variability. Our solution is implemented atop the Eclipse Modeling Framework and Sirius, and synthesizes adapted graphical editors integrated with Feature IDE.",

author = "Antonio Garmendia and Manuel Wimmer and Esther Guerra and Elena G{\'o}mez-Martin{\'e}z and \{de Lara\}, Juan",

year = "2020",

month = nov,

day = "16",

doi = "10.1145/3425898.3426957",

language = "English",

pages = "15--21",

editor = "Martin Erwig and Jeff Gray",

booktitle = "19th ACM SIGPLAN International Conference on Generative Programming, Concepts and Experiences (GPCE 2020), November 16–17, 2020, Chicago, USA, virtual",

}

Garmendia, A, Wimmer, M, Guerra, E, Gómez-Martinéz, E & de Lara, J 2020, Automated Variability Injection for Graphical Modelling Languages. in M Erwig & J Gray (eds), 19th ACM SIGPLAN International Conference on Generative Programming, Concepts and Experiences (GPCE 2020), November 16–17, 2020, Chicago, USA, virtual. pp. 15-21. https://doi.org/10.1145/3425898.3426957

Automated Variability Injection for Graphical Modelling Languages. / Garmendia, Antonio; Wimmer, Manuel; Guerra, Esther et al.
19th ACM SIGPLAN International Conference on Generative Programming, Concepts and Experiences (GPCE 2020), November 16–17, 2020, Chicago, USA, virtual. ed. / Martin Erwig; Jeff Gray. 2020. p. 15-21.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

TY - GEN

T1 - Automated Variability Injection for Graphical Modelling Languages

AU - Garmendia, Antonio

AU - Wimmer, Manuel

AU - Guerra, Esther

AU - Gómez-Martinéz, Elena

AU - de Lara, Juan

PY - 2020/11/16

Y1 - 2020/11/16

N2 - Model-based development approaches, such as Model-Driven Engineering (MDE), heavily rely on the use of modelling languages to achieve and automate software development tasks. To enable the definition of model variants (e.g., supporting the compact description of system families), one solution is to combine MDE with Software Product Lines. However, this is technically costly as it requires adapting many MDE artefacts associated to the modelling language - especially the meta-models and graphical environments. To alleviate this situation, we propose a method for the automated injection of variability into graphical modelling languages. Given the meta-model and graphical environment of a particular language, our approach permits configuring the allowed model variability, and the graphical environment is automatically adapted to enable creating models with variability. Our solution is implemented atop the Eclipse Modeling Framework and Sirius, and synthesizes adapted graphical editors integrated with Feature IDE.

AB - Model-based development approaches, such as Model-Driven Engineering (MDE), heavily rely on the use of modelling languages to achieve and automate software development tasks. To enable the definition of model variants (e.g., supporting the compact description of system families), one solution is to combine MDE with Software Product Lines. However, this is technically costly as it requires adapting many MDE artefacts associated to the modelling language - especially the meta-models and graphical environments. To alleviate this situation, we propose a method for the automated injection of variability into graphical modelling languages. Given the meta-model and graphical environment of a particular language, our approach permits configuring the allowed model variability, and the graphical environment is automatically adapted to enable creating models with variability. Our solution is implemented atop the Eclipse Modeling Framework and Sirius, and synthesizes adapted graphical editors integrated with Feature IDE.

UR - https://se.jku.at/automated-variability-injection-for-graphical-modelling-languages/

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DO - 10.1145/3425898.3426957

M3 - Conference proceedings

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EP - 21

BT - 19th ACM SIGPLAN International Conference on Generative Programming, Concepts and Experiences (GPCE 2020), November 16–17, 2020, Chicago, USA, virtual

A2 - Erwig, Martin

A2 - Gray, Jeff

ER -

Automated Variability Injection for Graphical Modelling Languages

Abstract

Fields of science

JKU Focus areas

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Other files and links

CDL-MINT Christian Doppler Laboratory for Model-Integrated Smart Production

Cite this

Automated Variability Injection for Graphical Modelling Languages

Abstract

Fields of science

JKU Focus areas

Access to Document

Other files and links

Projects

CDL-MINT Christian Doppler Laboratory for Model-Integrated Smart Production

Cite this