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| Funder | Vinnova |
|---|---|
| Recipient Organization | Mälardalen University College |
| Country | Sweden |
| Start Date | Apr 01, 2021 |
| End Date | Sep 30, 2024 |
| Duration | 1,278 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | Swedish Research Council |
| Grant ID | 2021-01364_Vinnova |
Purpose and goal:
To create a framework incorporating methods and tools for continuous software and system engineering and validation leveraging the advantages of AI techniques (notably Machine Learning) in order to provide benefits in significantly improved productivity, quality and predictability of CPSs, CPSoSs and, more generally, large and complex industrial systems.
Expected results and effects:
AIDOaRT aims to impact organizations where continuous deployment and operations management are standard operating procedures. DevOps teams may use the AIDOaRT framework to analyze event streams in real-time and historical data, extract meaningful insights from events for continuous improvement, drive faster deployments and better collaboration, and reduce downtime with proactive detection.
Approach and implementation: The project will be carried out following a continuous integration and continuous delivery (CI/CD) approach inspired by DevOps. The project plan will evolve continuously by considering the overall development and release process and the necessary adjustments to their plan. By following the DevOps practices, the project
will consider an iteration path: 1. Technology plan 2. Technology development 3. Technology integration 4. UC development 5. UC execution and validation 6. Evaluation & feedback
Mälardalen University College
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