DICE: Quality-driven development of data-intensive cloud applications

G. Casale; D. Ardagna; M. Artac; F. Barbier; E. Di Nitto; A. Henry; G. Iuhasz; C. Joubert; J. Merseguer; V. I. Munteanu; J. F. Pérez; D. Petcu; M. Rossi; C. Sheridan; I. Spais; D. Vladušič

doi:10.1109/MiSE.2015.21

DICE: Quality-driven development of data-intensive cloud applications

G. Casale, D. Ardagna, M. Artac, F. Barbier, E. Di Nitto, A. Henry, G. Iuhasz, C. Joubert, J. Merseguer, V. I. Munteanu, J. F. Pérez, D. Petcu, M. Rossi, C. Sheridan, I. Spais, D. Vladušič

Research output: Chapter in Book/Report › Conference contribution

54 Scopus citations

Abstract

Model-driven engineering (MDE) often features quality assurance (QA) techniques to help developers creating software that meets reliability, efficiency, and safety requirements. In this paper, we consider the question of how quality-aware MDE should support data-intensive software systems. This is a difficult challenge, since existing models and QA techniques largely ignore properties of data such as volumes, velocities, or data location. Furthermore, QA requires the ability to characterize the behavior of technologies such as Hadoop/MapReduce, NoSQL, and stream-based processing, which are poorly understood from a modeling standpoint. To foster a community response to these challenges, we present the research agenda of DICE, a quality-aware MDE methodology for data-intensive cloud applications. DICE aims at developing a quality engineering tool chain offering simulation, verification, and architectural optimization for Big Data applications. We overview some key challenges involved in developing these tools and the underpinning models.

Translated title of the contribution	DICE: Desarrollo basado en la calidad de aplicaciones de nube con uso intensivo de datos
Original language	English (US)
Title of host publication	Proceedings - 7th International Workshop on Modeling in Software Engineering, MiSE 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	78-83
Number of pages	6
ISBN (Electronic)	9781479919345
DOIs	https://doi.org/10.1109/MiSE.2015.21
State	Published - Jan 1 2015
Externally published	Yes
Event	7th International Workshop on Modeling in Software Engineering, MiSE 2015 - Florence, Italy Duration: May 16 2015 → May 17 2015

Conference

Conference	7th International Workshop on Modeling in Software Engineering, MiSE 2015
Country/Territory	Italy
City	Florence
Period	5/16/15 → 5/17/15

All Science Journal Classification (ASJC) codes

Modeling and Simulation
Software

Access to Document

10.1109/MiSE.2015.21

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Casale, G., Ardagna, D., Artac, M., Barbier, F., Di Nitto, E., Henry, A., Iuhasz, G., Joubert, C., Merseguer, J., Munteanu, V. I., Pérez, J. F., Petcu, D., Rossi, M., Sheridan, C., Spais, I., & Vladušič, D. (2015). DICE: Quality-driven development of data-intensive cloud applications. In Proceedings - 7th International Workshop on Modeling in Software Engineering, MiSE 2015 (pp. 78-83). Article 7167407 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MiSE.2015.21

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title = "DICE: Quality-driven development of data-intensive cloud applications",

abstract = "Model-driven engineering (MDE) often features quality assurance (QA) techniques to help developers creating software that meets reliability, efficiency, and safety requirements. In this paper, we consider the question of how quality-aware MDE should support data-intensive software systems. This is a difficult challenge, since existing models and QA techniques largely ignore properties of data such as volumes, velocities, or data location. Furthermore, QA requires the ability to characterize the behavior of technologies such as Hadoop/MapReduce, NoSQL, and stream-based processing, which are poorly understood from a modeling standpoint. To foster a community response to these challenges, we present the research agenda of DICE, a quality-aware MDE methodology for data-intensive cloud applications. DICE aims at developing a quality engineering tool chain offering simulation, verification, and architectural optimization for Big Data applications. We overview some key challenges involved in developing these tools and the underpinning models.",

author = "G. Casale and D. Ardagna and M. Artac and F. Barbier and {Di Nitto}, E. and A. Henry and G. Iuhasz and C. Joubert and J. Merseguer and Munteanu, {V. I.} and P{\'e}rez, {J. F.} and D. Petcu and M. Rossi and C. Sheridan and I. Spais and D. Vladu{\v s}i{\v c}",

year = "2015",

month = jan,

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doi = "10.1109/MiSE.2015.21",

language = "English (US)",

pages = "78--83",

booktitle = "Proceedings - 7th International Workshop on Modeling in Software Engineering, MiSE 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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note = "7th International Workshop on Modeling in Software Engineering, MiSE 2015 ; Conference date: 16-05-2015 Through 17-05-2015",

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Casale, G, Ardagna, D, Artac, M, Barbier, F, Di Nitto, E, Henry, A, Iuhasz, G, Joubert, C, Merseguer, J, Munteanu, VI, Pérez, JF, Petcu, D, Rossi, M, Sheridan, C, Spais, I & Vladušič, D 2015, DICE: Quality-driven development of data-intensive cloud applications. in Proceedings - 7th International Workshop on Modeling in Software Engineering, MiSE 2015., 7167407, Institute of Electrical and Electronics Engineers Inc., pp. 78-83, 7th International Workshop on Modeling in Software Engineering, MiSE 2015, Florence, Italy, 5/16/15. https://doi.org/10.1109/MiSE.2015.21

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AU - Di Nitto, E.

AU - Henry, A.

AU - Iuhasz, G.

AU - Joubert, C.

AU - Merseguer, J.

AU - Munteanu, V. I.

AU - Pérez, J. F.

AU - Petcu, D.

AU - Rossi, M.

AU - Sheridan, C.

AU - Spais, I.

AU - Vladušič, D.

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N2 - Model-driven engineering (MDE) often features quality assurance (QA) techniques to help developers creating software that meets reliability, efficiency, and safety requirements. In this paper, we consider the question of how quality-aware MDE should support data-intensive software systems. This is a difficult challenge, since existing models and QA techniques largely ignore properties of data such as volumes, velocities, or data location. Furthermore, QA requires the ability to characterize the behavior of technologies such as Hadoop/MapReduce, NoSQL, and stream-based processing, which are poorly understood from a modeling standpoint. To foster a community response to these challenges, we present the research agenda of DICE, a quality-aware MDE methodology for data-intensive cloud applications. DICE aims at developing a quality engineering tool chain offering simulation, verification, and architectural optimization for Big Data applications. We overview some key challenges involved in developing these tools and the underpinning models.

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PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

DICE: Quality-driven development of data-intensive cloud applications

Abstract

Conference

All Science Journal Classification (ASJC) codes

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