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...
| Autores: | , , , , , , , |
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| Formato: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2022 |
| País: | España |
| Recursos: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/147181 |
| Acesso em linha: | http://hdl.handle.net/10609/147181 http://doi.org/10.1016/j.micpro.2022.104672 |
| Access Level: | acceso embargado |
| Palavra-chave: | 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 |
| Resumo: | 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. |
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