The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project
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Abstract (arXiv)
Generative AI (GenAI) has recently emerged as a groundbreaking force in Software Engineering, capable of generating code, identifying bugs, recommending fixes, and supporting quality assurance. While its use in coding tasks shows considerable promise, applying GenAI across the entire Software Development Life Cycle (SDLC) has not yet been fully explored. Critical uncertainties in areas such as reliability, accountability, security, and data privacy demand deeper investigation and coordinated action. The GENIUS project, comprising over 30 European industrial and academic partners, aims to address these challenges by advancing AI integration across all SDLC phases. It focuses on GenAI's potential, the development of innovative tools, and emerging research challenges, actively 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 findings 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 key technological and methodological advances expected over the next five years; (3) anticipated shifts in the roles and required skill sets of software professionals; and (4) the contribution of GENIUS in realising 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, and industry-ready GenAI solutions for software engineering teams.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 44 of its 85 references that are in the map. First posted on arXiv 3 Nov 2025.
Builds on
44 of 85 references.
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32 more
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Built on by
Citing papers per year: 2026: 4.
4 papers.
- 2026Analyzing Message-Code Inconsistency in AI Coding Agent-Authored Pull Requestscited by 3
- 2026(How) Do Large Language Models Understand High-Level Message Sequence Charts?cited by 1
- 2026Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Studycited by 0
- 2026"Don't Be Afraid, Just Learn": Insights from Industry Practitioners to Prepare Software Engineers in the Age of Generative AIcited by 0
- 2026(How) Do Large Language Models Understand High-Level Message Sequence Charts?13 May 2026
- 2026"Don't Be Afraid, Just Learn": Insights from Industry Practitioners to Prepare Software Engineers in the Age of Generative AI7 Apr 2026
- 2026Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Study23 Jan 2026
- 2026Analyzing Message-Code Inconsistency in AI Coding Agent-Authored Pull Requests8 Jan 2026