The Future of Development Environments with AI Foundation Models: NII Shonan Meeting 222 Report

• URL: http://arxiv.org/abs/2511.16092v1
• Date: Thu, 20 Nov 2025 06:33:20 GMT
• Title: The Future of Development Environments with AI Foundation Models: NII Shonan Meeting 222 Report
• Authors: Xing Hu, Raula Gaikovina Kula, Christoph Treude,
• Abstract summary: The ability to increase the level of abstraction away from writing code has the potential to change the Human-AI interaction within the integrated development environment (IDE)<n>Experts from the Software Engineering, Artificial Intelligence, and Human-Computer Interaction domains gathered to discuss challenges and opportunities at Shonan Meeting 222.
• Score: 13.736653705740336
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative Artificial Intelligence (GenAI) models are achieving remarkable performance in various tasks, including code generation, testing, code review, and program repair. The ability to increase the level of abstraction away from writing code has the potential to change the Human-AI interaction within the integrated development environment (IDE). To explore the impact of GenAI on IDEs, 33 experts from the Software Engineering, Artificial Intelligence, and Human-Computer Interaction domains gathered to discuss challenges and opportunities at Shonan Meeting 222. This is the report

Related papers

• Forecasting Developer Environments with GenAI: A Research Perspective [35.06013188065995]
  The ability to increase the level of abstraction away from writing code has the potential to change the Human-AI interaction within the integrated development environment (IDE)<n>Experts from the Software Engineering, Artificial Intelligence, and Human-Computer Interaction domains gathered to discuss challenges and opportunities at Shonan Meeting 222.
  arXiv  Detail & Related papers  (2026-02-07T07:16:01Z)
• Report for NSF Workshop on AI for Electronic Design Automation [56.48556959103223]
  This report distills the discussions and recommendations from the NSF Workshop on AI for Electronic Design Automation (EDA)<n>Bringing together experts across machine learning and EDA, the workshop examined how AI-spanning large language models (LLMs), graph neural networks (GNNs), reinforcement learning (RL), neurosymbolic methods, etc.<n>The report recommends NSF to foster AI/EDA collaboration, invest in foundational AI for EDA, develop robust data infrastructures, promote scalable compute infrastructure, and invest in workforce development to democratize hardware design and enable next-generation hardware systems.
  arXiv  Detail & Related papers  (2026-01-20T23:45:40Z)
• Human-AI Experience in Integrated Development Environments: A Systematic Literature Review [2.1749194587826026]
  The integration of Artificial Intelligence into Integrated Development Environments (IDEs) is reshaping how developers interact with their tools.<n>This shift marks the emergence of Human-AI Experience in Integrated Development Environment (in-IDE HAX)<n>Research on in-IDE HAX remains fragmented, which highlights the need for a unified overview of current practices, challenges, and opportunities.
  arXiv  Detail & Related papers  (2025-03-08T12:40:18Z)
• Reflection on Data Storytelling Tools in the Generative AI Era from the Human-AI Collaboration Perspective [54.152639172274]
  Large-scale generative AI techniques have the potential to enhance data storytelling with their power in visual and narration generation.<n>We compare the collaboration patterns of the latest tools with those of earlier ones using a dedicated framework for understanding human-AI collaboration in data storytelling.<n>The benefits of these AI techniques and implications to human-AI collaboration are also revealed.
  arXiv  Detail & Related papers  (2025-03-04T13:56:18Z)
• How Developers Interact with AI: A Taxonomy of Human-AI Collaboration in Software Engineering [8.65285948382426]
  We propose a taxonomy of interaction types between developers and AI tools, identifying eleven distinct interaction types.<n>Building on this taxonomy, we outline a research agenda focused on optimizing AI interactions, improving developer control, and addressing trust and usability challenges in AI-assisted development.
  arXiv  Detail & Related papers  (2025-01-15T12:53:49Z)
• Future of Information Retrieval Research in the Age of Generative AI [61.56371468069577]
  In the fast-evolving field of information retrieval (IR), the integration of generative AI technologies such as large language models (LLMs) is transforming how users search for and interact with information.<n> Recognizing this paradigm shift, a visioning workshop was held in July 2024 to discuss the future of IR in the age of generative AI.<n>This report contains a summary of discussions as potentially important research topics and contains a list of recommendations for academics, industry practitioners, institutions, evaluation campaigns, and funding agencies.
  arXiv  Detail & Related papers  (2024-12-03T00:01:48Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• In-IDE Human-AI Experience in the Era of Large Language Models; A Literature Review [2.6703221234079946]
  The study of in-IDE Human-AI Experience is critical in understanding how these AI tools are transforming the software development process. We conducted a literature review to study the current state of in-IDE Human-AI Experience research.
  arXiv  Detail & Related papers  (2024-01-19T14:55:51Z)
• Exploring the intersection of Generative AI and Software Development [0.0]
  The synergy between generative AI and Software Engineering emerges as a transformative frontier. This whitepaper delves into the unexplored realm, elucidating how generative AI techniques can revolutionize software development. It serves as a guide for stakeholders, urging discussions and experiments in the application of generative AI in Software Engineering.
  arXiv  Detail & Related papers  (2023-12-21T19:23:23Z)
• From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape [5.852005817069381]
  The study critically examined the current state and future trajectory of generative Artificial Intelligence (AI) It explored how innovations like Google's Gemini and the anticipated OpenAI Q* project are reshaping research priorities and applications across various domains. The study highlighted the importance of incorporating ethical and human-centric methods in AI development, ensuring alignment with societal norms and welfare.
  arXiv  Detail & Related papers  (2023-12-18T01:11:39Z)
• Exploration with Principles for Diverse AI Supervision [88.61687950039662]
  Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI. While this generative AI approach has produced impressive results, it heavily leans on human supervision. This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation. We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
  arXiv  Detail & Related papers  (2023-10-13T07:03:39Z)
• Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
  We show how AI can empower the IoT to make it faster, smarter, greener, and safer. First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
  arXiv  Detail & Related papers  (2020-11-17T13:14:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.