1. AIDOaRt: AI-augmented Automation for DevOps, a Model-based Framework for Continuous Development in Cyber-Physical Systems
- Author
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Antonio Cicchetti, Andrey Sadovykh, Abel Gómez, Alessandra Bagnato, Hugo Bruneliere, Romina Eramo, Vittoriano Muttillo, Luca Berardinelli, University of L'Aquila [Italy] (UNIVAQ), Johannes Kepler Universität Linz - Johannes Kepler University Linz [Autriche] (JKU), Département Automatique, Productique et Informatique (IMT Atlantique - DAPI), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), NaoMod - Nantes Software Modeling Group (LS2N - équipe NaoMod), Laboratoire des Sciences du Numérique de Nantes (LS2N), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Universitat Oberta de Catalunya [Barcelona] (UOC), SOFTEAM, Mälardalen University (MDH), European Project: 101007350,AIDOaRt, Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Nantes Université (Nantes Univ), Johannes Kepler University Linz [Linz] (JKU), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), NaoMod - Nantes Software Modeling Group (NaoMod), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
Computer science ,Computer Networks and Communications ,Information technology operations ,Context (language use) ,[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE] ,Cyber-Physical Systems ,Continuous development ,System engineering ,Software engineering ,Model Driven Engineering ,Artificial Intelligence ,DevOps ,AIOps ,[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL] ,Continuous System Engineering ,AIOPS ,Scientific computing ,Simulation and Modelling Tools ,business.industry ,Software development ,Cyber-physical system ,Software deployment ,Hardware and Architecture ,Systems design ,business ,Engineering design process ,Software - Abstract
International audience; 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 modelling, 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.
- Published
- 2021