DICE: Quality-Driven Development of Data-Intensive Cloud Applications

被引：31

作者：

Casale, G. ^{[1
]}

Ardagna, D. ^{[2
]}

Artac, M. ^{[3
]}

Barbier, F. ^{[4
]}

Di Nitto, E. ^{[2
]}

Henry, A. ^{[4
]}

Iuhasz, G. ^{[7
]}

Joubert, C. ^{[5
]}

Merseguer, J. ^{[6
]}

Munteanu, V. I. ^{[7
]}

Perez, J. F. ^{[1
]}

Petcu, D. ^{[7
]}

Rossi, M. ^{[2
]}

Sheridan, C. ^{[8
]}

Spais, I. ^{[9
]}

Vladusic, D. ^{[3
]}

机构：

[1] Imperial Coll London, London, England

[2] Politecn Milan, Milan, Italy

[3] XLAB, Ljubljana, Slovenia

[4] Netfective, Pessac, France

[5] Prodevelop, Valencia, Spain

[6] Univ Zaragoza, E-50009 Zaragoza, Spain

[7] Inst E Austria Timisoara, Timisoara, Romania

[8] Flexiant Technol, London, England

[9] Athens Technol Ctr SA, Athens, Greece

来源：

2015 IEEE/ACM 7TH INTERNATIONAL WORKSHOP ON MODELING IN SOFTWARE ENGINEERING | 2015年

关键词：

Big Data; quality assurance; model-driven engineering;

D O I：

10.1109/MiSE.2015.21

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页码：78 / 83

页数：6

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