DICE · Project

Quality Engineering Toolkit That Helps Software Companies Build Reliable Big Data Cloud Apps Faster

digitalTestedTRL 5

dice-h2020.eu

Imagine you run a small software company and a client wants you to build an app that crunches massive amounts of data in the cloud — think real-time analytics or large-scale processing. The problem is, making sure that app actually works reliably under pressure is extremely hard and expensive, especially for smaller teams. DICE built an open-source toolkit that lets developers design, test, and deploy these data-heavy cloud applications with built-in quality checks at every step — like having a co-pilot that catches problems before they reach your customers. It covers everything from early design simulation to automated testing and deployment.

By the numbers

EUR 3,954,484

EU funding invested in building the open-source toolkit

consortium partners who developed and validated the tools

countries represented in the development consortium

SME partners with direct industry experience

total deliverables produced including tools, documentation, and reports

demo deliverables with released tool implementations

60%

industry ratio in the consortium

The business problem

What needed solving

Small and medium software companies want to build data-intensive cloud applications using Big Data technologies like Hadoop, NoSQL, and stream processing — but ensuring these apps meet the quality and reliability standards required for business-critical use is expensive and technically demanding. Many ISVs lack the resources and expertise for advanced quality engineering, which blocks them from competing in the growing Big Data market.

The solution

What was built

DICE produced an open-source quality engineering toolchain with 12 demo deliverables covering simulation tools (performance prediction), verification tools (safety evaluation), optimization tools (cost-minimized deployment), testing tools (automated testing with fault injection), delivery tools (DevOps deployment and continuous integration), and anomaly detection tools (runtime trace checking). All components were released in multiple iterations and integrated into a unified DICE toolkit.

Audience

Who needs this

Small and medium ISVs building data-intensive cloud applicationsCloud consulting firms deploying Big Data solutions for enterprise clientsEnterprise IT departments migrating data processing to cloud infrastructureDevOps teams managing Hadoop, NoSQL, or stream processing deploymentsQuality assurance teams responsible for cloud application reliability

Business applications

Who can put this to work

Software Development & ISVs

SME

Target: Small and medium independent software vendors building cloud-based data products

If you are a small or mid-size software vendor struggling to guarantee quality in your Big Data applications — DICE developed an open-source toolchain with simulation, verification, and optimization tools that let you catch performance and reliability issues during the design phase, not after deployment. The project involved 10 partners across 7 countries, with 6 SME participants who validated the tools against real development workflows.

Financial Services & Fintech

enterprise

Target: Banks and fintech companies processing large transaction datasets in the cloud

If you are a financial services firm running data-intensive processing on cloud infrastructure and worried about reliability and compliance — DICE created verification and anomaly detection tools that evaluate the safety of data-intensive applications before they go live. The toolkit uses industry standards like UML and TOSCA, making it easier to integrate into existing development pipelines.

IT Consulting & Cloud Services

mid-size

Target: Cloud consulting firms and managed service providers deploying Big Data solutions for clients

If you are a cloud services provider deploying Hadoop, NoSQL, or stream processing solutions for enterprise clients — DICE built delivery and deployment tools with DevOps-inspired continuous integration, plus a cost optimization engine for architecture and deployment plans. The full toolkit was released as open source with 35 deliverables documenting its capabilities.

Frequently asked

Quick answers

What would it cost to adopt these tools?

The DICE toolkit was released as open source, so licensing costs are zero. The project was funded with EUR 3,954,484 in EU contributions across 10 partners, meaning significant R&D investment went into building these tools at no cost to adopters. Integration and training costs would depend on your team's familiarity with UML, MARTE, and TOSCA standards.

Can these tools handle production-scale workloads?

The tools were designed specifically for business-critical data-intensive applications using Big Data technologies like Hadoop/MapReduce, NoSQL, and stream processing. The toolchain went through multiple development iterations (initial, intermediate, and final versions) and was validated by 6 SME partners in the consortium. However, the project ended in 2018, so current maintenance status should be verified.

What is the IP and licensing situation?

The DICE toolkit was released as open source, as stated in the deliverable descriptions. This means you can use, modify, and integrate the tools freely. Since 6 of the 10 consortium partners were SMEs with industry backgrounds, the tools were designed with commercial software development in mind.

How does DICE integrate with existing development workflows?

DICE was built on widely-used standards — UML for modeling, MARTE for performance annotations, and TOSCA for cloud deployment descriptions. The DevOps-inspired delivery and testing tools support continuous integration pipelines. This standards-based approach was chosen specifically to lower the adoption barrier for small and medium ISVs.

Is the project still active and supported?

The project officially ended in January 2018. The open-source releases and 35 deliverables remain available. Based on available project data, ongoing community maintenance would need to be verified through the project website at dice-h2020.eu or the source code repositories.

What specific quality problems does it solve?

DICE addresses performance prediction through simulation, safety verification for data-intensive applications, cost optimization of deployment architectures, automated testing with fault injection, and runtime anomaly detection with trace checking. Each capability has dedicated tools that went through at least two release cycles during the project.

Consortium

Who built it

The DICE consortium of 10 partners across 7 countries (UK, Spain, Italy, France, Greece, Romania, Slovenia) has a strong industry orientation with 60% industry ratio and 6 SME participants. This is significant — it means the tools were shaped by companies that actually build software for a living, not just academics. Imperial College London coordinated the project, bringing research credibility, while the 6 industry partners ensured the tools addressed real development pain points. The geographic spread across Southern, Western, and Eastern Europe suggests the tools were tested against diverse market conditions and development practices.

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINECoordinator · UK
ATHENS TECHNOLOGY CENTER ANONYMI VIOMICHANIKI EMPORIKI KAI TECHNIKI ETAIREIA EFARMOGON YPSILIS TECHNOLOGIASparticipant · EL
UNIVERSIDAD DE ZARAGOZAparticipant · ES
XLAB RAZVOJ PROGRAMSKE OPREME IN SVETOVANJE DOOparticipant · SI
INSTITUTUL E-AUSTRIA TIMISOARAparticipant · RO
PRODEVELOP SLparticipant · ES
NETFECTIVE TECHNOLOGY SAparticipant · FR
FLEXIANT LIMITEDparticipant · UK
FLEXIOPS LIMITEDparticipant · UK
POLITECNICO DI MILANOparticipant · IT

How to reach the team

The project was coordinated by Imperial College London. SciTransfer can facilitate an introduction to the research team.

Order a report on this consortium See IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE profile →

Next steps

Talk to the team behind this work.

Want to evaluate whether the DICE quality engineering tools fit your development pipeline? SciTransfer can arrange a technical briefing with the team that built them and help you assess integration feasibility.

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