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Model-driven Engineering IDE for Quality Assessment of Data-intensive Applications

Authors:

Marc Gil,

Christophe Joubert,

Ismael TorresAuthors Info & Claims

ICPE '17 Companion: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion

Pages 173 - 174

https://doi.org/10.1145/3053600.3053633

Published: 18 April 2017 Publication History

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Abstract

This article introduces a model-driven engineering (MDE) integrated development environment (IDE) for Data-Intensive Cloud Applications (DIA) with iterative quality enhancements. As part of the H2020 DICE project (ICT-9-2014, id 644869), a framework is being constructed and it is composed of a set of tools developed to support a new MDE methodology. One of these tools is the IDE which acts as the front-end of the methodology and plays a pivotal role in integrating the other tools of the framework. The IDE enables designers to produce from the architectural structure of the general application along with their properties and QoS/QoD annotations up to the deployment model. Administrators, quality assurance engineers or software architects may also run and examine the output of the design and analysis tools in addition to the designer in order to assess the DIA quality in an iterative process.

References

[1]

G. Casale, D. Ardagna, M. Artac, F. Barbier, E. D. Nitto, A. Henry, G. Iuhasz, C. Joubert, J. Merseguer, V. I. Munteanu, J. F. Prez, D. Petcu, M. Rossi, C. Sheridan, I. Spais, D. Vladui, "Dice: Quality-driven development of data-intensive cloud applications", Proceedings of the 7th International Workshop on Modeling in Software Engineering (MiSE 2015), 2015.

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[2]

Simona Bernardi, José Ignacio Requeno, Christophe Joubert, and Alberto Romeu. 2016. A systematic approach for performance evaluation using process mining: the POSIDONIA operations case study. In Proceedings of the 2nd International Workshop on Quality-Aware DevOps (QUDOS 2016). ACM, New York, NY, USA, 24--29.

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Gómez AJoubert CCabot J(2023)Blockchain Technologies in the Design and Operation of Cyber-Physical SystemsDigital Transformation10.1007/978-3-662-65004-2_9(223-243)Online publication date: 3-Feb-2023
https://doi.org/10.1007/978-3-662-65004-2_9
Zafar MAzam FRehman SAnwar M(2017)A Systematic Review of Big Data Analytics Using Model Driven EngineeringProceedings of the 2017 International Conference on Cloud and Big Data Computing10.1145/3141128.3141138(1-5)Online publication date: 17-Sep-2017
https://dl.acm.org/doi/10.1145/3141128.3141138

Index Terms

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Published In

ICPE '17 Companion: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion

April 2017

248 pages

ISBN:9781450348997

DOI:10.1145/3053600

General Chairs:
Walter Binder
Università della Svizzera Italiana, Switzerland
,
Vittorio Cortellessa
Università dell'Aquila, Italy
,
Program Chairs:
Anne Koziolek
Karlsruhe Institute of Technology, Germany
,
Evgenia Smirni
College of William & Mary, USA
,
Meikel Poess
Oracle Corporation, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 April 2017

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Short-paper

Funding Sources

European Union under the H2020 Research and Innovation Program

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ICPE '17

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ICPE '17: ACM/SPEC International Conference on Performance Engineering

April 22 - 26, 2017

L'Aquila, Italy

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ICPE '17 Companion Paper Acceptance Rate 24 of 65 submissions, 37%;

Overall Acceptance Rate 252 of 851 submissions, 30%

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Cited By

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Gómez AJoubert CCabot J(2023)Blockchain Technologies in the Design and Operation of Cyber-Physical SystemsDigital Transformation10.1007/978-3-662-65004-2_9(223-243)Online publication date: 3-Feb-2023
https://doi.org/10.1007/978-3-662-65004-2_9
Zafar MAzam FRehman SAnwar M(2017)A Systematic Review of Big Data Analytics Using Model Driven EngineeringProceedings of the 2017 International Conference on Cloud and Big Data Computing10.1145/3141128.3141138(1-5)Online publication date: 17-Sep-2017
https://dl.acm.org/doi/10.1145/3141128.3141138

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Model-driven engineering

Model-driven engineering with domain-specific meta-modelling languages

Model-driven Engineering in the Development of Ubiquitous Applications: Technologies, Tools and Languages