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What we do

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).


The growing complexity of CPS poses several challenges throughout all software development during their usage and maintenance.

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 will 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 decisions and process and complete system development tasks. AI technological innovations have to ensure that systems are designed responsibly contributes to our trust in their behavior, 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).

Our 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 (such as modeling, coding, testing, and monitoring).
Supporting the monitoring of runtime data (such as logs, events, and metrics), software data, and traceability (Observe). Analyzing both data of historical and real-time data (Analyze) and the automation of functionality (Automate).

Our mission

Our 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.

Expected impact

We expect an industrial uptake of AIDOaRt technologies on the development of complex systems that scales up to real systems demand with relevance for all critical applications.

The overarching goal of AIDOaRt is to support requirements, monitoring, modeling, coding, and testing as part of a continuous system engineering (CSE) in Cyber-Physical Systems (CPS) and systems 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 analyzing collected data from both runtime and design time artifacts in rapid CSE cycles.