mlsanityData to 7 Oct 2026

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

arXiv 2511.01348 ↗First posted 3 Nov 2025cs.SE cs.AIcited by 4

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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.

References · 44

  1. 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 1,906
  2. 2024Training Large Language Models to Reason in a Continuous Latent Spacecited by 763
  3. 2024A Survey on Large Language Models for Code Generationcited by 646
  4. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 389
  5. 2025Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directionscited by 257
  6. 2025Why Language Models Hallucinatecited by 205
  7. 2024Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judgescited by 197
  8. 2023Large Language Models for Software Engineering: Survey and Open Problemscited by 178
  9. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  10. 2024LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Aheadcited by 155
  11. 2025Measuring AI Ability to Complete Long Software Taskscited by 142
  12. 2023Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Modelscited by 130
32 more
  1. 2023The Metacognitive Demands and Opportunities of Generative AIcited by 85
  2. 2024From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Futurecited by 85
  3. 2022Natural Attack for Pre-trained Models of Codecited by 72
  4. 2023LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Modelscited by 71
  5. 2023A Survey on Large Language Models for Software Engineeringcited by 58
  6. 2024Grammar-Aligned Decodingcited by 54
  7. 2023Bias Testing and Mitigation in LLM-based Code Generationcited by 53
  8. 2024Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmapcited by 30
  9. 2024MARE: Multi-Agents Collaboration Framework for Requirements Engineeringcited by 28
  10. 2023Generative Artificial Intelligence for Software Engineering -- A Research Agendacited by 23
  11. 2024The Future of AI-Driven Software Engineeringcited by 23
  12. 2024The Current Challenges of Software Engineering in the Era of Large Language Modelscited by 21
  13. 2025A Study of LLMs' Preferences for Libraries and Programming Languagescited by 20
  14. 2025Unified Software Engineering Agent as AI Software Engineercited by 17
  15. 2024SecCoder: Towards Generalizable and Robust Secure Code Generationcited by 13
  16. 2024Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Toolscited by 13
  17. 2024Security Threats in Agentic AI Systemcited by 11
  18. 2024LLMs: A Game-Changer for Software Engineers?cited by 11
  19. 2024Deep Learning-based Software Engineering: Progress, Challenges, and Opportunitiescited by 9
  20. 2024OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Modelscited by 8
  21. 2024From Today's Code to Tomorrow's Symphony: The AI Transformation of Developer's Routine by 2030cited by 8
  22. 2025Human and Machine: How Software Engineers Perceive and Engage with AI-Assisted Code Reviews Compared to Their Peerscited by 8
  23. 2024Using Grammar Masking to Ensure Syntactic Validity in LLM-based Modeling Taskscited by 6
  24. 2025Towards Advancing Code Generation with Large Language Models: A Research Roadmapcited by 5
  25. 2024Natural Language-Oriented Programming (NLOP): Towards Democratizing Software Creationcited by 4
  26. 2024"Should I Give Up Now?" Investigating LLM Pitfalls in Software Engineeringcited by 4
  27. 2025Comparative Analysis of Carbon Footprint in Manual vs. LLM-Assisted Code Developmentcited by 4
  28. 2024Ethics of Software Programming with Generative AI: Is Programming without Generative AI always radical?cited by 2
  29. 2024Large Language Models as Robust Data Generators in Software Analytics: Are We There Yet?cited by 2
  30. 2025SENAI: Towards Software Engineering Native Generative Artificial Intelligencecited by 2
  31. 2024Some things never change: how far generative AI can really change software engineering practicecited by 1
  32. 2024The Rise and Fall(?) of Software Engineeringcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.