arXiv:2512.05666cs.LGcs.AI2025-12被引 1

非程序员用AI生成代码,能快速做出网页应用。

Feasibility of AI-Assisted Programming for End-User Development

  • 让非程序员通过自然语言指令让AI写代码开发应用。
  • 多数参与者在合理时间内完成任务并认可该方法可行。
  • 适合想快速建应用的普通用户或教学场景使用。

终端用户开发(end-user development)指非程序员自行创建或修改数字工具,对组织数字化转型具有重要意义。目前,低代码/无代码平台通过可视化编程广泛支持此类开发,减少手动编码需求。近年来,基于大语言模型的生成式AI助手和‘协作编程’工具的进展,为用户提供新可能:可通过自然语言提示直接生成和优化代码、构建应用程序。这种称为AI辅助终端用户编码的方法,相比传统可视化低代码平台,有望实现更高的灵活性、更广的应用范围、更快的开发速度、更好的可复用性以及更低的供应商锁定风险。本文探讨该方法是否具备作为终端用户开发可行范式的潜力,可能补充甚至替代现有低代码模式。为此,我们开展了一项案例研究,让非程序员借助AI助手开发一个基础网页应用。大多数参与者在合理时间内成功完成任务,并表示支持该方法作为终端用户开发的有效途径。论文呈现了研究设计、结果分析,并讨论其对实践、未来研究和学术教学的潜在影响。

原文摘要 · Abstract (English)

End-user development,where non-programmers create or adapt their own digital tools, can play a key role in driving digital transformation within organizations. Currently, low-code/no-code platforms are widely used to enable end-user development through visual programming, minimizing the need for manual coding. Recent advancements in generative AI, particularly large language model-based assistants and "copilots", open new possibilities, as they may enable end users to generate and refine programming code and build apps directly from natural language prompts. This approach, here referred to as AI-assisted end-user coding, promises greater flexibility, broader applicability, faster development, improved reusability, and reduced vendor lock-in compared to the established visual LCNC platforms. This paper investigates whether AI-assisted end-user coding is a feasible paradigm for end-user development, which may complement or even replace the LCNC model in the future. To explore this, we conducted a case study in which non-programmers were asked to develop a basic web app through interaction with AI assistants.The majority of study participants successfully completed the task in reasonable time and also expressed support for AI-assisted end-user coding as a viable approach for end-user development. The paper presents the study design, analyzes the outcomes, and discusses potential implications for practice, future research, and academic teaching.

AI编程终端用户开发低代码自然语言生成

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