A Vision from Industry and Academia in the European GENIUS Project

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[Submitted on 3 Nov 2025 (v1), last revised 6 Nov 2025 (this version, v2)]

Authors:Robin Grobler, Steven Klepke, Jack Jones, Andreas Dreschinski, Klaus Schmid, Benedikt Dornauer, Eray Tozun, Joost Nobin, Mohammad Reza Mousavi, Yongjian Tang, Johannes Vihmann, Selin Şerin Aslangül, Bom Suk Lee, Adam Ziolkowski, Eric Xie.

View PDF of the article The Future of Generative AI in Software Engineering: A View from Industry and Academia in the European Genius Project, by Robin Grobler and 14 other authors

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a summary:Generative AI (GenAI) has recently emerged as a leading force in software engineering, capable of generating code, identifying bugs, recommending fixes, and supporting quality assurance. While its use in programming tasks shows great promise, the application of GenAI across the software development life cycle (SDLC) has not yet been fully explored. Critical uncertainties in areas such as reliability, accountability, security and data privacy require deeper investigation and coordinated action. The GENIUS project, which includes more than 30 European industrial and academic partners, aims to address these challenges by strengthening the integration of AI across all stages of the SDLC. It focuses on the potential of GenAI, the development of innovative tools, emerging research challenges, and effectively shaping the future of software engineering. This vision paper presents a shared perspective on the future of GenAI-driven software engineering, grounded in cross-sector dialogue as well as experiences and outcomes within the GENIUS Consortium. The paper explores four central elements: (1) a structured overview of current challenges in GenAI adoption across the SDLC; (2) a forward-looking vision outlining the major technological and methodological developments expected over the next five years; (3) expected shifts in the roles and skill sets required for software professionals; and (4) GENIUS’s contribution to achieving this transformation through practical tools and industrial validation. This paper focuses on aligning technical innovation with business relevance. It aims to inform both research agendas and industrial strategies, providing a foundation for reliable, scalable, industry-ready GenAI solutions for software engineering teams.