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AIDOaRt: AI-augmented Automation for DevOps, a model-based framework for continuous development in Cyber–Physical Systems
IMT Atlantique, LS2N (UMR CNRS 6004), Nantes, France.
University of L'Aquila, L'Aquila, Italy.
University of L'Aquila, L'Aquila, Italy.
Johannes Kepler University, Linz, Austria.
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2022 (English)In: Microprocessors and microsystems, ISSN 0141-9331, E-ISSN 1872-9436, Vol. 94, article id 104672Article in journal (Refereed) Published
Abstract [en]

The advent of complex Cyber–Physical Systems (CPSs) creates the need for more efficient engineering processes. Recently, DevOps promoted the idea of considering a closer continuous integration between system development (including its design) and operational deployment. Despite their use being still currently limited, Artificial Intelligence (AI) techniques are suitable candidates for improving such system engineering activities (cf. AIOps). In this context, AIDOaRT is a large European collaborative project that aims at providing AI-augmented automation capabilities to better support the modeling, coding, testing, monitoring, and continuous development of CPSs. The project proposes to combine Model Driven Engineering principles and techniques with AI-enhanced methods and tools for engineering more trustable CPSs. The resulting framework will (1) enable the dynamic observation and analysis of system data collected at both runtime and design time and (2) provide dedicated AI-augmented solutions that will then be validated in concrete industrial cases. This paper describes the main research objectives and underlying paradigms of the AIDOaRt project. It also introduces the conceptual architecture and proposed approach of the AIDOaRt overall solution. Finally, it reports on the actual project practices and discusses the current results and future plans.

Place, publisher, year, edition, pages
Elsevier B.V. , 2022. Vol. 94, article id 104672
Keywords [en]
AIOps, Artificial Intelligence, Continuous development, Cyber–Physical Systems, DevOps, Model Driven Engineering, Software engineering, System engineering, AIOp, Continuous integrations, Cybe-physical systems, Cyber-physical systems, Engineering process, Model-based OPC, Model-driven Engineering, Operational deployments, System development, Systems engineering
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-60083DOI: 10.1016/j.micpro.2022.104672ISI: 000872530400002Scopus ID: 2-s2.0-85138071342OAI: oai:DiVA.org:mdh-60083DiVA, id: diva2:1701203
Available from: 2022-10-05 Created: 2022-10-05 Last updated: 2023-05-24Bibliographically approved

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