
Basic data
- Industry 4.0
IVACE, ECSEL - European Commission H2020
AI-augmented automation for efficient DevOps, a model-based framework for continuous development At RunTime in cyber-physical systems
IVACE, ECSEL - European Commission H2020
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.