AI-augmented automation for efficient DevOps, a model-based framework for continuous development At RunTime in cyber-physical systems

Informacje na temat projektu

AIDOaRt

Identyfikator umowy o grant: 101007350

DOI

10.3030/101007350

Projekt został zamknięty

Data podpisania przez KE 12 Maja 2021

Data rozpoczęcia 1 Kwietnia 2021

Data zakończenia 30 Września 2024

Finansowanie w ramach

INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)

Koszt całkowity

€ 22 543 755,19

Wkład UE

€ 6 769 785,68

6 769 785,68

15 773 969,51

Koordynowany przez

MALARDALENS UNIVERSITET
Sweden

Periodic Reporting for period 3 - AIDOaRt (AI-augmented automation for efficient DevOps, a model-based framework for continuous development At RunTime in cyber-physical systems)

Okres sprawozdawczy: 2023-04-01 do 2024-09-30

Modern systems in the domains of Industry 4.0 health care, autonomously driving cars, or smart grids are examples of highly communicating (embedded) systems where software enables increasingly advanced functionality. The growing complexity of these Cyber-Physical Systems (CPS) and Cyber-Physical Systems of Systems (CPSoS) poses several challenges throughout all system design, development and analysis phases, but also during their deployment, actual usage, and future maintenance. There has not been any general and reusable AI-augmented approach intending to support full continuous software and system engineering processes in the context of different use cases and application domains.

AIDOaRt wants to impact organizations where continuous deployment and operations management are standard operating procedures. DevOps teams may use the AIDOaRt solutions to analyze event streams together with the design information to extract meaningful insights for system continuous development improvement, to drive faster deployments and foster better collaboration, and to reduce downtime. The integration of AI techniques can affect the whole development process. Thus, AIDOaRt provides a holistic approach for continuous systems engineering that:
(i) Provides a core model-based framework to support the CPS continuous systems engineering process.
(ii) Enhances the corresponding DevOps tool chain.
(iii) Supports the collection, representation and traceability of runtime data and software models (Observe), assists in the analysis of both historical and real time data in combination with design information (Analyze), and supports the automation of tasks of the DevOps pipeline according to the previous analysis (Automate).

The AIDOaRt mission is to create a framework incorporating methods and tools for continuous software and system engineering and validation leveraging the advantages of AI techniques (notably Machine Learning) in order to provide benefits in significantly improved productivity, quality and predictability of CPSs, CPSoSs and, more generally, large and complex industrial systems.

The project has a significant impact on the partners’ competitive advantage, growth, and internationalization.

During the first part of the project, the performed work focused on different complementary aspects aiming at triggering the project’s research and development activities.

1. Collection and expression of the various (industrial) use case requirements.
2. Specification of a first and second/final version of the overall AIDOaRt architecture.
3. Study of the scientific state-of-the-art in the main areas of interest of the project.
4. In ADIOaRt the use cases and solutions are represented in an overall data model, the AIDOaRt platform / framework, defined in the Universal Modeling Language tool MODELIO.
5. In the context of previous actions, various research experiments and technical developments
The AIDOarRt project team successfully organized 5 hackathons, which bridged the use-case providers and solution providers, and all the results were presented and communicated in the final AIDOm@rket event. AIDOaRt initiated the AI transformations—AIDOaRt Research and Industrial Forum, which was held on September 11 2024 in Stockholm, Sweden. This event attracted more than 50 attendees from academia and industry. Meanwhile, partners actively participated in various events to promote the project outcomes. For the event participation, AIDoaRt partners actively participated in 62 events, which included the scientific conferences, workshops, exhibitions, student visits etc. The AIDOaRt social media channels, at the end of the year, showed remarkable performances, the subscribers of linkedin Newsletter is up to 368. The scientific papers are up to 96.

The exploitation and standardization plans and actions performed during the reporting period to analyze the potential AIDOaRt partners' exploitation assets and the agreed individual and joint exploitation strategies as they have been included in our Project Consortium Agreement were delivered. in the final year, the exploitation work achieved the following in the final year:
1) Value propositions and exploitation strategies developed identified and described the exploitable outcomes and the potential users (target groups) for each of the AIDOaRt beneficiaries at M42
2) Development of Final Individual Exploration Plans
3) Development of a Financial Plans
4) Development of an Action Plan for each individual exploitation and each use case
5) Reasoning for the Potential Development of After-Project Exploitation Agreements
6) Analysis of all the project business models
7) Analysis of the technology readiness level and innovation type of the 43 project-developed solutions.

Also, during this period, partners of AIDOaRt have been working on the standardization efforts for MARTE and SysML, two of the modeling standards mentioned in the proposal.

The progress beyond the state-of-the-art affects data management and storage, Model-Driven Engineering for continuous software and system engineering, and AI/ML for continuous software and system engineering. The AIDOaRt framework will support inherent heterogeneity and the fast-paced evolution of a continuous collection of engineering data, providing both process- and product-related information, tailoring its data collection and management services to the analysis needs of other technical areas. It also addresses the main challenge of introducing an MDE process for DevOps, augmented with AI/ML mechanisms.

The foreseen socio-economic impact AIDOaRt is expected to contribute to two of the most challenging sectors, i.e. automotive and transportation (long term impact 17%) focussing on productivity gain obtained by higher automation of the software engineering process, which is a shared goal of AIOps (AI applied to DevOps) and MDE. By enhancing the competitiveness and sustainability of engineering processes, AIDOaRt will directly improve the quality of CPS/CPSoS products under development. This will, in turn, foster job creation in the IT design, development, and operations sectors. As highlighted in the Factories of the Future 2020 Roadmap, the societal impact of such projects extends beyond employment, also contributing to social sustainability.

The impact in the scientific community includes 96 scientific papers, including 21 journal papers, 70 publications in conference and workshop proceedings. For the general public, The project website as the main presence of project outlet has achieved over 4770 unique per year. The followers of the online social media community (LinkedIn and Twitter) is over 648. The project partners actively participated in various events, included the scientific conferences and workshops, which extended the impact to the large. AIDOaRt project also contributes to standardisation and normalisation bodies in order to increase interoperability and boost the adoption of the technology proposed by the project. In this regard, besides the preparation and retrieval of responses from partners in the Innovation and Standardization questionnaire, during this period, the work focused on two modelling standard specifications: MARTE and SysML. The final year's exploitation task focuses on strategies to disseminate and eventually commercialise the innovation. Hence, the AIDOaRt WP6 team organised 2 business model innovation workshops among the 31 partners.

AIDOaRt broshure

AIDOaRt roll-up at EFECT event

AIDOaRt approach overview

AIDOaRt global approach

AIDOaRt posters at EFECT event

AIDOaRt WP structure

Periodic Reporting for period 3 - AIDOaRt (AI-augmented automation for efficient DevOps, a model-based framework for continuous development At RunTime in cyber-physical systems)

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