AI-augmented automation for efficient DevOps, a model-based framework for continuous development At RunTime in cyber-physical systems
Public support
Provider
Ministry of Education, Youth and Sports
Programme
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Call for proposals
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Main participants
Vysoké učení technické v Brně / Fakulta informačních technologií
Contest type
M2 - International cooperation
Contract ID
MSMT-17739/2021-3/15
Alternative language
Project name in Czech
AI-augmented automation for efficient DevOps, a model-based framework for continuous development At RunTime in cyber-physical systems
Annotation in Czech
The project targets the development of a model-based framework to support teams during the automated continuous development of CPSs by means of integrated AI-augmented solutions. The overall AIDOaRT infrastructure will work with existing data sources, including traditional IT monitoring, log events, along with software models and measurements. The infrastructure is intended to operate within the DevOps process combining software development and information technology (IT) operations. Moreover, AI technological innovations have to ensure that systems are designed responsibly and contribute to our trust in their behaviour.
Scientific branches
R&D category
AP - Applied research
OECD FORD - main branch
20206 - Computer hardware and architecture
OECD FORD - secondary branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
OECD FORD - another secondary branch
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CEP - equivalent branches <br>(according to the <a href="http://www.vyzkum.cz/storage/att/E6EF7938F0E854BAE520AC119FB22E8D/Prevodnik_oboru_Frascati.pdf">converter</a>)
AF - Documentation, librarianship, work with information<br>BC - Theory and management systems<br>BD - Information theory<br>IN - Informatics<br>JC - Computer hardware and software
Completed project evaluation
Provider evaluation
V - Vynikající výsledky projektu (s mezinárodním významem atd.)
Project results evaluation
FIT VUT has successfully completed its participation in the European Chips JU project AIDOaRt. The resulting system uses machine learning methods to optimize parameter settings and to fuse sensor data, e.g. from radar and camera.
Solution timeline
Realization period - beginning
Apr 1, 2021
Realization period - end
Sep 30, 2024
Project status
U - Finished project
Latest support payment
Feb 10, 2024
Data delivery to CEP
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data delivery code
CEP25-MSM-8A-U
Data delivery date
Jun 27, 2025
Finance
Total approved costs
7,352 thou. CZK
Public financial support
4,778 thou. CZK
Other public sources
0 thou. CZK
Non public and foreign sources
2,573 thou. CZK