AIDOaRt – AI-augmented automation supporting modelling, coding, testing, monitoring and continuous development in Cyber-Physical Systems

AIDOaRt is a 3 years long H2020-ECSEL European project involving 32 organizations, grouped in clusters from 7 different countries, focusing on AI-augmented automation supporting modelling, coding, testing, monitoring and continuous development in Cyber-Physical Systems (CPS).

Many leading companies have started envisaging the automation of tomorrow to be brought about by full-blown Artificial Intelligence (AI) tech. While the number of companies that invest significant resources in software development is constantly increasing, the development and design techniques are still immature.

The overall AIDOaRt infrastructure aims to work with existing data sources, including traditional IT monitoring, log events, application and more. The infrastructure is intended to work within the DevOps practices combining software development and information technology (IT) operations.

The project aims at using AIOps to automate decision and process and complete system development tasks. AI technological innovations has to ensure that systems are designed responsibly contributes to our trust in their behaviour, and requires both accountabilities, i.e. being able to explain and justify decisions, and explainability, i.e., internal mechanics can be trusted and easily understood by humans).

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 overarching goal of AIDOaRt is to support requirements, monitoring, modelling, coding, and testing as part of a continuous system engineering (CSE) in Cyber-Physical Systems (CPS) and System of Systems (CPSoS) via AI-augmented automation. AIDOaRt proposes enhancing the engineering process with AI-augmented methods (A IOps), integrating DevOps and Model-Driven Engineering (MDE) principles, and observing and analysing collected data from both runtime and design time artefacts in rapid CSE cycles.

As Gartner reported in 2019, by 2023 40% of Infrastructure & Operations teams will use AI-augmented automation in large enterprises, resulting in higher IT productivity. AI-augmented automation supporting continuous development in complex systems promises vast and long-term economic value added in ultra-large system development. AIDOaRt targets the following main impact objectives:

  • Providing a model-based framework to support the CPS development process by introducing AI-augmented automation.
  • Enhancing the DevOps toolchain by employing AI and Machine Learning (ML) technique in multiple aspects of the system development process (as modelling, coding, testing and monitoring).
  • Supporting the monitoring of runtime data (such as logs, events and metrics), software data and traceability (Observe), the analysis of both data of historical and real-time data (Analyze) and the automation of functionality (Automate).


More info available at:

Project Reference Card

Title: AIDOaRt – AI-augmented automation supporting modelling, coding, testing, monitoring and continuous development in Cyber-Physical Systems
Researcher's beneficiary organization: Universitat Oberta de Catalunya
Other beneficiary organizations:
AIT Austrian Institute of Technology GMBH, Automated Software Testing GmbH, AVL List GMBH, Dynatrace Austria GmbH, Graz University of Technology, Johannes Kepler University Linz, BRNE University of Technology, CAMEA SPOL SRO, Anders Innovations, Åbo Akademi, Qentinel Oy, Clearsy SAS, Institut Mines-Telecom,, Softeam, Abinsula SRL, Intecs Solutions SPA, Ro Technology SRL, TEKNE SRL, University of L'Aquila, University of Sassari, ACORDE Technologies SA, HI iberia Ingenieria Y Proyectos SL, ITI Instituto Tecnologico de Informatica, Prodevelop, University of Cantabria, Alstom AB, Mälardalen University, RISE Research Institutes of Sweden, Volvo Contruction Equipment AB, Westermo Network Technologie
Duration: 36 months Start date: April 1, 2021
End date: March 31, 2024
Area: European Union Project type: Competitive R&D project
Funding entity: ECSEL - JU
Reference: 101007350 Programme: ECSEL-2020-2-RIA
Overall budget: 22,968,349 €
Type of participation: Researcher
Project URL: