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AI4SWEng

Horizon Europe · 2024 – 2028

AI4SWEng — AI Engineering Suite to support Agile Efficient Software Engineering

Financement : 5 255 326 €

16 partenaires

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AI4SWEng represents an excellent chemistry of 15 Partners including experts and practitioners in Model-Drive Software Engineering and AI Engineering (particularly LLMs) from across Europe and USA with four use-cases from Software Development, Healthcare, Cyber-Physical System involving multi-architectural and resource-constrained systems and Next Generation Battery Management in EVs. The AI4SWEng mission is to significantly contribute to the transformation of agile software development into a sector turbocharged for design-by-contact high efficiency, reliability, and security-privacy compliant delivery. This is to exploit advanced AI-powered software development support to deliver high-quality reliable fast time-to-market solutions that are socially responsible, scalable, sustainable and lend themselves to audible ethical and regulatory compliance. AI4SWEng aims deliver AI-powered support to software developers to help reduce worker stress past the pain-points in agile SW development pipelines and higher productivity thus enhanced job satisfaction and creativity. Accordingly, the project will develop, deliver and validate, in 4 use-cases, a platform comprising a set of tools including AISysDev and Test Automation Tool to support the software lifecycle end-to-end for both functional and non-functional deliverables by supporting a host of tasks such as code generation, cross-compilation advanced debugging and dynamic slicing, adaptation, security-privacy by design, reliability, energy efficiency and adaptation as well as project management, process quality and maintenance support through data-driven insights for informed design decision-making and strategic software development planning Additionally with a commitment to advance user-centred design, it will provide structured design in advanced prompt engineering to support software developers team to best exploit to capabilities of the platform.

artificial intelligence
large language models
tinyml
code generation
reverse execution
forward execution
self supervised learning

Structures participantes de Université Paris-Saclay

Provenance des données

Sources : scanR. Données consolidées le 27/10/2026. Une erreur ? Elle se corrige dans la source d’origine (HAL, RNSR, annuaire) ; voir comment.