研究程序员如何用大模型做编程助手，发现信任与上下文是关键
Conversational AI as a Coding Assistant: Understanding Programmers' Interactions with and Expectations from Large Language Models for Coding
- 通过调查分析程序员使用大模型编码助手的交互方式
- 发现准确率不足、缺乏上下文是主要障碍，部分人因信任问题拒绝使用
- 建议设计应增强上下文记忆、透明度和用户偏好适应性
基于大语言模型（LLMs）的对话式AI编程助手日益普及。然而，程序员如何与这些助手互动、面临哪些挑战、影响采纳的因素仍不明确。本研究通过问卷调查，探讨程序员在使用LLM驱动的编程助手时的使用模式、感知及策略。参与者指出其优势如提升效率、解释更清晰，但也存在准确性不足、缺乏上下文感知、对过度依赖的担忧等问题。值得注意的是，部分程序员因偏好独立学习、不信任AI生成代码或出于伦理考量而主动回避使用。基于研究发现，提出改进对话式编程助手的设计指南，强调上下文保持、透明性、多模态支持以及对用户偏好的自适应能力。这些见解有助于推动LLM驱动的对话代理在软件开发流程中的有效集成，同时应对采纳障碍，提升可用性。
原文摘要 · Abstract (English)
Conversational AI interfaces powered by large language models (LLMs) are increasingly used as coding assistants. However, questions remain about how programmers interact with LLM-based conversational agents, the challenges they encounter, and the factors influencing adoption. This study investigates programmers' usage patterns, perceptions, and interaction strategies when engaging with LLM-driven coding assistants. Through a survey, participants reported both the benefits, such as efficiency and clarity of explanations, and the limitations, including inaccuracies, lack of contextual awareness, and concerns about over-reliance. Notably, some programmers actively avoid LLMs due to a preference for independent learning, distrust in AI-generated code, and ethical considerations. Based on our findings, we propose design guidelines for improving conversational coding assistants, emphasizing context retention, transparency, multimodal support, and adaptability to user preferences. These insights contribute to the broader understanding of how LLM-based conversational agents can be effectively integrated into software development workflows while addressing adoption barriers and enhancing usability.
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