A Rapid Quality‐Aware Development of Data-Intensive Cloud Applications

Abstract

In this paper, we deliberate the query of howquality-aware MDE should provision data-intensive softwaresystems. This is a difficult challenge, since current models andQA techniques largely ignore properties of data such asvolumes, velocities, or data location. Additionally, QA necessitatesthe ability to characterize the behavior of technologies such asHadoop/MapReduce, NoSQL, and stream-based processing,which are poorly understood from a modeling standpoint. Tofoster a community response to these challenges, we presentthe research agenda of DICE, a quality-aware MDEmethodology for data-intensive cloud applications. DICE aimsat developing a quality engineering tool chain offeringsimulation, verification, and architectural optimization for BigData applications.