Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
An architecture for model-based and intelligent automation in DevOps
The increasing complexity of modern systems poses numerous challenges at all stages of system development and operation. Continuous software and system engineering processes, e.g., DevOps, are increasingly adopted and spread across organizations. In parallel, many leading companies have begun to apply artificial intelligence (AI) principles and techniques, including Machine Learning (ML), to improve their products. However, there is no holistic approach that can support and enhance the growing challenges of DevOps. In this paper, we propose a software architecture that provides the foundations of a model-based framework for the development of AI-augmented solutions incorporating methods and tools for continuous software and system engineering and validation. The key characteristic of the proposed architecture is that it allows leveraging the advantages of both AI/ML and Model Driven Engineering (MDE) approaches and techniques in a DevOps context. This architecture has been designed, developed and applied in the context of the European large collaborative project named AIDOaRt. In this paper, we also report on the practical evaluation of this architecture. This evaluation is based on a significant set of technical solutions implemented and applied in the context of different real industrial case studies coming from the AIDOaRt project. Moreover, we analyze the collected results and discuss them according to both architectural and technical challenges we intend to tackle with the proposed architecture. ; The work presented in this paper is funded by the ECSEL Joint Undertaking (JU)under grant agreement No. 101007350 (AIDOaRt project). The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Sweden, Austria, Czech Republic, Finland, France, Italy, Spain. ; Peer Reviewed ; Postprint (published version)
An architecture for model-based and intelligent automation in DevOps
The increasing complexity of modern systems poses numerous challenges at all stages of system development and operation. Continuous software and system engineering processes, e.g., DevOps, are increasingly adopted and spread across organizations. In parallel, many leading companies have begun to apply artificial intelligence (AI) principles and techniques, including Machine Learning (ML), to improve their products. However, there is no holistic approach that can support and enhance the growing challenges of DevOps. In this paper, we propose a software architecture that provides the foundations of a model-based framework for the development of AI-augmented solutions incorporating methods and tools for continuous software and system engineering and validation. The key characteristic of the proposed architecture is that it allows leveraging the advantages of both AI/ML and Model Driven Engineering (MDE) approaches and techniques in a DevOps context. This architecture has been designed, developed and applied in the context of the European large collaborative project named AIDOaRt. In this paper, we also report on the practical evaluation of this architecture. This evaluation is based on a significant set of technical solutions implemented and applied in the context of different real industrial case studies coming from the AIDOaRt project. Moreover, we analyze the collected results and discuss them according to both architectural and technical challenges we intend to tackle with the proposed architecture. ; The work presented in this paper is funded by the ECSEL Joint Undertaking (JU)under grant agreement No. 101007350 (AIDOaRt project). The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Sweden, Austria, Czech Republic, Finland, France, Italy, Spain. ; Peer Reviewed ; Postprint (published version)
An architecture for model-based and intelligent automation in DevOps
Eramo, Romina (Autor:in) / Said, Bilal (Autor:in) / Oriol Hilari, Marc (Autor:in) / Brunelière, Hugo (Autor:in) / Morales, Sergio (Autor:in) / Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació / Universitat Politècnica de Catalunya. inSSIDE - integrated Software, Services, Information and Data Engineering
01.11.2024
doi:10.1016/j.jss.2024.112180
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DevOps Competences for Smart City Administrators
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