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AIDOaRt

Financiado

Cerrado

AI augmented automation for efficient DevOps a model based framework for contin...

AI augmented automation for efficient DevOps a model based framework for continuous development At RunTime in cyber physical systems The project idea is focusing on AI-augmented automation supporting modeling, coding, testing, and monitoring as part of a continuous development in Cyber-Physical Systems (CPSs). The growing complexity of CPS poses several challen... ver más

30/09/2024

MDU

23M€

Presupuesto del proyecto: 23M€

Líder del proyecto

MALARDALENS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

Ver 32 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2024-09-30

ECSEL-2020-2-RIA: ECSEL-2020-2-RIA

I+D

Cerrada hace 4 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

32 Participantes

MDU

785.84K€ | Lider

AUTOMATED SOFTWARE TESTING G...

184.54K€ | Participante

Åbo Akademi University

335.69K€ | Participante

INTECS SOLUTIONS

112.50K€ | Participante

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Duración del proyecto: 40 meses Fecha Inicio: 2021-05-12
Fecha Fin: 2024-09-30

Líder del proyecto

MALARDALENS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 23M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The project idea is focusing on AI-augmented automation supporting modeling, coding, testing, and monitoring as part of a continuous development in Cyber-Physical Systems (CPSs). The growing complexity of CPS poses several challenges throughout all software development and analysis phases, but also during their usage and maintenance. Many leading companies have started envisaging the automation of tomorrow to be brought about by Artificial Intelligence (AI) tech. While the number of companies that invest significant resources in software development is constantly increasing, the use of AI in the development and design techniques is still immature. 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 (i.e., requiring both accountability and explainability). 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.

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