Developing Data-Intensive Applications with Iterative Qualitative Enhancements
Started in 1 February 2015, for 36 months
Official site: dice-h2020.eu/
DICE will offer a novel UML profile and tools that will help software designers reasoning about reliability, safety and efficiency of Big Data applications. The DICE methodology will cover quality assessment, architecture enhancement, continuous testing and agile delivery, relying on principles of the emerging DevOps paradigm.
In order to support the development of high-quality data-intensive applications, DICE aims at:
- Tackling skill shortage and steep learning curves in quality-driven development and Big Data technologies through open source development tools, models, and methods.
- Shortening the time to market for data-intensive applications that meet quality requirements, thus reducing costs for independent software vendors, while increasing value for end users.
- Reducing the number and the severity of quality-related incidents by iteratively learning application runtime behavior, feeding back the information to the developers.
- Imperial College London (UK),
- Politecnico di Milano (Italy),
- Institutul e-Austria Timisoara (Romania),
- XLAB (Slovenia),
- Flexiant (UK),
- ATC (Greece),
- ProDevelop (Spain),
- NetEffective (France),
- Universidad Zaragoza (Spain),