AIDOaRt: AI-augmented Automation for DevOps, a model-based framework for continuous development in Cyber–Physical Systems

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...

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Detalles Bibliográficos
Autores: Bruneliere, Hugo, Muttillo, Vittoriano, Eramo, Romina, Berardinelli, Luca, Gómez, Abel, Bagnato, Alessandra, Sadovykh, Andrey, Cicchetti, Antonio
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2022
País:España
Institución:Universitat Oberta de Catalunya (UOC)
Repositorio:O2, repositorio institucional de la UOC
OAI Identifier:oai:openaccess.uoc.edu:10609/147181
Acceso en línea:http://hdl.handle.net/10609/147181
http://doi.org/10.1016/j.micpro.2022.104672
Access Level:acceso embargado
Palabra clave:cyber–physical systems
continuous development
system engineering
software engineering
model driven engineering
artificial intelligence
DevOps
AIOps
sistemas ciberfísicos
desarrollo continuo
ingeniería de sistemas
ingeniería de software
ingeniería basada en modelos
inteligencia artificial
sistemes ciberfísics
desenvolupament continu
enginyeria de sistemes
enginyeria de programari
enginyeria basada en models
intel·ligència artificial
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spelling AIDOaRt: AI-augmented Automation for DevOps, a model-based framework for continuous development in Cyber–Physical SystemsBruneliere, HugoMuttillo, VittorianoEramo, RominaBerardinelli, LucaGómez, AbelBagnato, AlessandraSadovykh, AndreyCicchetti, Antoniocyber–physical systemscontinuous developmentsystem engineeringsoftware engineeringmodel driven engineeringartificial intelligenceDevOpsAIOpssistemas ciberfísicosdesarrollo continuoingeniería de sistemasingeniería de softwareingeniería basada en modelosinteligencia artificialDevOpsAIOpssistemes ciberfísicsdesenvolupament continuenginyeria de sistemesenginyeria de programarienginyeria basada en modelsintel·ligència artificialDevOpsAIOpsartificial intelligenceintel·ligència artificialinteligencia artificialThe 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.ElsevierIMT AtlantiqueUniversità degli Studi dell'AquilaJohannes Kepler University LinzUniversitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)SOFTEAMMälardalen University202320232022info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10609/147181http://doi.org/10.1016/j.micpro.2022.104672reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)InglésMicroprocessors and Microsystems, 2022, 9494;https://doi.org/10.1016/j.micpro.2022.104672info:eu-repo/grantAgreement/EC/H2020/101007350CC BY-NC-ND 4.0http://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/embargoedAccessoai:openaccess.uoc.edu:10609/1471812026-05-28T12:42:01Z
dc.title.none.fl_str_mv AIDOaRt: AI-augmented Automation for DevOps, a model-based framework for continuous development in Cyber–Physical Systems
title AIDOaRt: AI-augmented Automation for DevOps, a model-based framework for continuous development in Cyber–Physical Systems
spellingShingle AIDOaRt: AI-augmented Automation for DevOps, a model-based framework for continuous development in Cyber–Physical Systems
Bruneliere, Hugo
cyber–physical systems
continuous development
system engineering
software engineering
model driven engineering
artificial intelligence
DevOps
AIOps
sistemas ciberfísicos
desarrollo continuo
ingeniería de sistemas
ingeniería de software
ingeniería basada en modelos
inteligencia artificial
DevOps
AIOps
sistemes ciberfísics
desenvolupament continu
enginyeria de sistemes
enginyeria de programari
enginyeria basada en models
intel·ligència artificial
DevOps
AIOps
artificial intelligence
intel·ligència artificial
inteligencia artificial
title_short AIDOaRt: AI-augmented Automation for DevOps, a model-based framework for continuous development in Cyber–Physical Systems
title_full AIDOaRt: AI-augmented Automation for DevOps, a model-based framework for continuous development in Cyber–Physical Systems
title_fullStr AIDOaRt: AI-augmented Automation for DevOps, a model-based framework for continuous development in Cyber–Physical Systems
title_full_unstemmed AIDOaRt: AI-augmented Automation for DevOps, a model-based framework for continuous development in Cyber–Physical Systems
title_sort AIDOaRt: AI-augmented Automation for DevOps, a model-based framework for continuous development in Cyber–Physical Systems
dc.creator.none.fl_str_mv Bruneliere, Hugo
Muttillo, Vittoriano
Eramo, Romina
Berardinelli, Luca
Gómez, Abel
Bagnato, Alessandra
Sadovykh, Andrey
Cicchetti, Antonio
author Bruneliere, Hugo
author_facet Bruneliere, Hugo
Muttillo, Vittoriano
Eramo, Romina
Berardinelli, Luca
Gómez, Abel
Bagnato, Alessandra
Sadovykh, Andrey
Cicchetti, Antonio
author_role author
author2 Muttillo, Vittoriano
Eramo, Romina
Berardinelli, Luca
Gómez, Abel
Bagnato, Alessandra
Sadovykh, Andrey
Cicchetti, Antonio
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv IMT Atlantique
Università degli Studi dell'Aquila
Johannes Kepler University Linz
Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
SOFTEAM
Mälardalen University
dc.subject.none.fl_str_mv cyber–physical systems
continuous development
system engineering
software engineering
model driven engineering
artificial intelligence
DevOps
AIOps
sistemas ciberfísicos
desarrollo continuo
ingeniería de sistemas
ingeniería de software
ingeniería basada en modelos
inteligencia artificial
DevOps
AIOps
sistemes ciberfísics
desenvolupament continu
enginyeria de sistemes
enginyeria de programari
enginyeria basada en models
intel·ligència artificial
DevOps
AIOps
artificial intelligence
intel·ligència artificial
inteligencia artificial
topic cyber–physical systems
continuous development
system engineering
software engineering
model driven engineering
artificial intelligence
DevOps
AIOps
sistemas ciberfísicos
desarrollo continuo
ingeniería de sistemas
ingeniería de software
ingeniería basada en modelos
inteligencia artificial
DevOps
AIOps
sistemes ciberfísics
desenvolupament continu
enginyeria de sistemes
enginyeria de programari
enginyeria basada en models
intel·ligència artificial
DevOps
AIOps
artificial intelligence
intel·ligència artificial
inteligencia artificial
description 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10609/147181
http://doi.org/10.1016/j.micpro.2022.104672
url http://hdl.handle.net/10609/147181
http://doi.org/10.1016/j.micpro.2022.104672
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Microprocessors and Microsystems, 2022, 94
94;
https://doi.org/10.1016/j.micpro.2022.104672
info:eu-repo/grantAgreement/EC/H2020/101007350
dc.rights.none.fl_str_mv CC BY-NC-ND 4.0
http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/embargoedAccess
rights_invalid_str_mv CC BY-NC-ND 4.0
http://creativecommons.org/licenses/by-nc-nd/4.0
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:O2, repositorio institucional de la UOC
instname:Universitat Oberta de Catalunya (UOC)
instname_str Universitat Oberta de Catalunya (UOC)
reponame_str O2, repositorio institucional de la UOC
collection O2, repositorio institucional de la UOC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869407156518780928
score 15,301603

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