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.