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AIDOaRt

AI-augmented automation for efficient DevOps, a model-based framework for continuous development At RunTime in cyber-physical systems

Field
European
Date
01/04/2021 - 01/03/2024
Industry
  • Industry 4.0
Budget
320113,63
Funded by

IVACE, ECSEL - European Commission H2020

Website
Video

PROJECT INFORMATION

DESCRIPTION

AIDOaRT aims to improve the DevOps toolchain by employing Artificial Intelligence (AI) techniques, in particular Machine Learning (ML), in multiple aspects of the systems engineering process (design and modeling quality, source code performance, test coverage and predictive monitoring, among others). According to AIOps, the set of tools to be developed in AIDOaRT should support:
- Collecting and monitoring run-time data (logs, events and metrics) and software data (design models), as well as managing their traceability. See
- Real-time and historical data analysis. Analyze
- Guidance and automation of design and development operations. Automa

The project aims to use AIOps to provide decision support tools and support various system engineering tasks.

AIDOaRT aims to improve the DevOps toolchain by employing Artificial Intelligence (AI) techniques, in particular Machine Learning (ML), in multiple aspects of the systems engineering process (design and modeling quality, source code performance, test coverage and predictive monitoring, among others). According to AIOps, the set of tools to be developed in AIDOaRT should support:
– Collecting and monitoring run-time data (logs, events and metrics) and software data (design models), as well as managing their traceability. See
– Real-time and historical data analysis. Analyze
– Guidance and automation of design and development operations. Automa

The project aims to use AIOps to provide decision support tools and support various system engineering tasks.

Technological capabilities

Big Data
Data collection and sensing