| 1. |
- Bagnato, A., et al.
(författare)
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AI-Augmented Model-Based Capabilities in the AIDOaRt Project : Continuous Development of Cyber-physical Systems
- 2022
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Ingår i: Ada User Journal. - : Ada-Europe. - 1381-6551. ; 43:4, s. 230-234
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Tidskriftsartikel (refereegranskat)abstract
- The paper presents the AIDOaRt project, a 3 years long H2020-ECSEL European project involving 32 organizations, grouped in clusters from 7 different countries, focusing on AI-augmented automation supporting modeling, coding, testing, monitoring, and continuous development in Cyber-Physical Systems (CPS). To this end, the project proposes to combine Model Driven Engineering principles and techniques with AI-enhanced methods and tools for engineering more trustable and reliable CPSs. This paper introduces the AIDOaRt project, its overall objectives, and used requirement engineering methodology. Based on that, it also focuses on describing the current plan regarding a set of tools intended to cover the model-based capabilities requirements from the project.
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| 2. |
- Bruneliere, H., et al.
(författare)
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AIDOaRt : AI-augmented Automation for DevOps, a model-based framework for continuous development in Cyber–Physical Systems
- 2022
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Ingår i: Microprocessors and microsystems. - : Elsevier B.V.. - 0141-9331 .- 1872-9436. ; 94
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Tidskriftsartikel (refereegranskat)abstract
- 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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