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

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Autores
Casale, Giuliano
Ardagna, Danilo
Artac, Matej
Barbier, Franck
Di Nitto, Elisabetta
Henry, Alexis
Pérez, Juan F.

Fecha
2015-07-27

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IEEE

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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.
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Keywords
Unified modeling language , Big data , Data models , Computational modeling , Analytical models , Reliability , Software
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