作为编程助手的对话式AI：理解程序员与用于编程的大语言模型的交互及对其的期望
Conversational AI as a Coding Assistant: Understanding Programmers' Interactions with and Expectations from Large Language Models for Coding
AI总结：
本研究调研程序员使用LLM驱动的对话式编程助手的模式、认知与策略，梳理了工具的优劣势与用户拒绝使用的原因，提出设计准则，为该类工具融入软件开发流程提供参考。
摘要：
由大语言模型（LLM）驱动的对话式AI界面正越来越多地被用作编程助手。然而，关于程序员如何与基于LLM的对话智能体交互、他们会遇到哪些挑战，以及影响该技术采纳的因素有哪些，目前仍存在疑问。本研究调查了程序员在使用LLM驱动的编程助手时的使用模式、认知感受与交互策略。通过一项调研，受访者既提及了这类工具的优势，比如效率提升、解释清晰，也指出了其局限性，包括结果不准确、缺乏上下文感知能力，以及对过度依赖该工具的担忧。值得注意的是，部分程序员主动选择不使用LLM，原因包括他们更偏好独立学习、不信任AI生成的代码，以及出于伦理层面的考量。基于研究结果，我们提出了改进对话式编程助手的设计准则，重点关注上下文留存、透明度、多模态支持，以及对用户偏好的适配性。这些发现有助于更广泛地理解如何将基于LLM的对话智能体有效整合到软件开发工作流中，同时解决采纳障碍并提升可用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
