The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project

2025-11-03

AI Summary

The integration of generative AI (GenAI) into the software development lifecycle (SDLC) faces critical uncertainties regarding reliability, accountability, security, and data privacy. Method: This study—conducted collaboratively by over 30 European industry, academic, and research institutions—establishes a comprehensive GenAI adoption vision and a co-innovation framework spanning requirements, design, coding, testing, and operations. It introduces a five-year technology roadmap and develops a verifiable toolchain integrating code generation, quality assurance, security analysis, and trustworthy AI. Contribution/Results: The project delivers cross-sector consensus-based practice guidelines and functional prototypes, enabling the reliable, scalable deployment of AI-driven software engineering. It further supports the transformation and reskilling of software engineers to meet evolving role demands in GenAI-augmented development environments.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Application Domains: Software Engineering

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Generative AI (GenAI) has recently emerged as a groundbreaking force in Software Engineering, capable of generating code, suggesting 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-based software engineering, grounded in cross-sector dialogue and experience within the GENIUS consortium, supported by an exploratory literature review. 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 realizing this transformation through practical tools and industrial validation. By aligning technical innovation with business relevance, this paper aims to inform both research agendas and industrial strategies, providing a foundation for reliable, scalable, and industry-ready GenAI solutions for software engineering teams.

Problem

• Exploring GenAI application across entire Software Development Life Cycle
• Addressing reliability, accountability, security and data privacy uncertainties
• Developing practical GenAI tools for industry-ready software engineering solutions

Innovation

• Advancing AI integration across all SDLC phases
• Developing innovative tools for software engineering teams
• Providing reliable and scalable GenAI industrial solutions