AI 中文总结
本文指出AI虽扩大代码生成获取范围但或使软件控制权集中，软件工程专业知识重心转向意图规范，并提出教育、工具和政策领域的研究机遇以应对AI时代挑战。
AI 中文摘要
生成式人工智能很快将编写所有代码的说法，引发了编程即将走向终结的预测。在这篇愿景论文中，我们反对“更广泛获取代码生成工具必然会使软件开发民主化”的假设，即认为每个人都能编程，但我们必须区分“获取”与“控制”：“获取”指更多人（包括非专家和经验较少的开发者）能借助AI生成类代码制品；“控制”指将这些制品作为可靠软件进行检查、评估、集成、维护和治理的能力。尽管AI可能扩大代码生成的获取范围，但控制权可能会集中在那些拥有或理解代码、软件实践、基础设施、评估流程及部署流水线的人手中。基于专家小组的研究，本文指出AI并未消除软件工程专业知识，而是改变了该专业知识最关键的领域：软件工程专业知识的重心正转向意图规范，即协调与管控AI行为、评估软件行为及集成软件系统。最后，本文确定了教育、工具和政策领域的研究机会，以帮助软件工程界在AI时代拥有更强的自主性、问责制和适应性。
英文摘要
Claims that generative AI will soon write all of the code have led to predictions that programming is nearing its end. In this vision paper, we argue against this assumption that broader access to code generation necessarily democratizes software development, i.e., everyone can code but we have to distinguish between access and control: by access, we mean the ability of more people, including non-experts and less-experienced developers, to generate code-like artifacts with AI; by control, we mean the capacity to inspect, evaluate, integrate, maintain, and govern those artifacts as dependable software. While AI may broaden access to code production, control may become more concentrated among those who own or understand the code, software practices, infrastructure, evaluation practices, and deployment pipelines. Grounded in an expert panel, our vision paper argues that AI does not eliminate software engineering expertise but shifts where that expertise becomes most critical. The locus of software engineering expertise is shifting toward intent specification: orchestrating and governing AI behavior, evaluating software behavior, and integrating software systems. We conclude this paper by identifying research opportunities for education, tools, and policy that can help the software engineering community respond to the AI era with greater agency, accountability, and adaptability.
CommentsAccepted to the ACM AI Leadership Summit 2026
