一句话：基于22位开发者访谈提出依赖控制框架，用控制层级识别AI过度依赖与依赖不足。
论文关注软件开发者在工作中使用AI工具（尤其是LLM）时的依赖程度问题：过度依赖可能导致批判性思维萎缩等长期负面后果，而依赖不足又会牺牲生产力与质量收益。作者通过对22位使用LLM进行软件开发的开发者进行访谈，提出一个初步的“依赖控制框架”，用控制层级来识别和区分AI过度依赖与依赖不足。该框架还被用于建议未来研究应探索当前及新兴LLM驱动工具所支持的不同控制层级。论文为AI过度依赖的新兴讨论作出贡献，强调理解“适当依赖程度”对开发者充分利用这些强大技术至关重要，并为实践者、教育者和政策制定者提供负责任且有效使用AI工具的启示。
How software developers interact with Artificial Intelligence (AI)-powered tools, including Large Language Models (LLMs), plays a vital role in how these AI-powered tools impact them. While overreliance on AI may lead to long-term negative consequences (e.g., atrophy of critical thinking skills); underreliance might deprive software developers of potential gains in productivity and quality. Based on twenty-two interviews with software developers on using LLMs for software development, we propose a preliminary reliance-control framework where the level of control can be used as a way to identify AI overreliance and underreliance. We also use it to recommend future research to further explore the different control levels supported by the current and emergent LLM-driven tools. Our paper contributes to the emerging discourse on AI overreliance and provides an understanding of the appropriate degree of reliance as essential to developers making the most of these powerful technologies. Our findings can help practitioners, educators, and policymakers promote responsible and effective use of AI tools.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2604-10530,
title = {Towards an Appropriate Level of Reliance on AI: A Preliminary Reliance-Control Framework for AI in Software Engineering},
author = {Samuel Ferino and Rashina Hoda and John Grundy and Christoph Treude},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3806704}
}