AIDOaRt is a European project that gathers 32 organizations from 7 different countries to focus on creating AI-augmented automation supporting modeling, coding, testing, monitoring, and continuous development in Cyber-Physical Systems (CPS). The project will last for three years, and the major goal of AIDOaRt is to provide a model-based framework to more efficiently support the continuous software and system engineering of CPSs and CPSoS via AI augmentation.
Anders’ role in the project is to work as a tech provider, developing AIDOaRt’s DevOps functionalities in cooperation with other partners. We have been developing our DevOps capabilities in the past few years and have invested in developing our know-how so the project offers a first-class opportunity to expand our knowledge even further.
About the project
Modern systems in the domains of Industry 4.0, health care, autonomously driving cars, or smart grids are examples of highly communicating (embedded) systems where software enables increasingly advanced functionality.
The growing complexity of these Cyber-Physical Systems (CPS) and Cyber-Physical Systems of Systems (CPSoS) pose several challenges throughout all system design, development, and analysis phases and during their deployment, actual usage, and future maintenance.
Nowadays, more and more companies are deploying AI in some specific parts of their businesses, or at least testing it in the context of proofs of concept or (often limited) internal trials. AI for IT operations can evolve by enabling DevOps to embrace the scale and speed of the latest state-of-the-art practices. By means of AIOps, we can reimagine the DevOps pipeline through continuous monitoring, alerting, and remediation in a secure and reliable way.
The project has three objectives: to provide a model-based framework to support CPS development process by introducing AI-augmented automation, enhancing the DevOps toolchain by employing AI and Machine Learning (ML) and supporting the monitoring of runtime data.
The mission is “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) to provide benefits in significantly improved productivity, quality and predictability of CPSs CPSoSs and, more generally, large and complex industrial systems.”
The 32 organizations are divided into three sections:
- Industrial partners
- Academic partners
- Tech providers
Industrial partners (e.g. Volvo, AVL, and Nordstrom) provide the use cases, in other words, the problems they would like to have solved. Academic partners, such as Åbo Akademi, will do what they do best, research, develop novel ideas, and write research papers about their findings. Technology providers support the rest of the partners with tools, expertise, and software development efforts.
This project allows us to leverage our expertise in software engineering and artificial intelligence in new research challenges on the development of cyber-physical systems. I believe this project presents a great opportunity for Åbo Akademi researchers to collaborate with many leading companies and top research groups from 7 different European countries.
- Ivan Porres, Proffessor, Åbo Akademi University
Read more: www.aidoart.eu
We at Anders see this AIDOaRt project as a great opportunity to be at a very forefront of new technological development and to be a part of creating something genuinely innovative. Participating in this project also gives us leverage to expand our international footprint and strengthen our DevOps market position. We are eager to learn more about DevOps and how AIOps increase the software development even further.
AIDOaRt wants to impact organizations where continuous deployment and operations management are standard operating procedures. DevOps teams may use the AIDOaRt framework to analyze event streams together with the design information to extract meaningful insights for system continuous development improvement, drive faster deployments, foster better collaboration, and reduce downtime with proactive detection.
I see the project as a great opportunity fo a better future for software development and a for everyone by, in the end, being able to help the industry develop better products and processes.
- Frank Wickström, CTO, Anders Innovations