PwR：探索表示在对话式编程中的作用
PwR: Exploring the Role of Representations in Conversational Programming
AI总结：
PwR 通过自然语言表示将系统理解反馈给用户，以弥合对话式编程中用户心智模型与 AI 实际理解之间的差距，实验表明其能显著提升可理解性和用户自主感。
摘要：
大型语言模型（LLMs）已经彻底改变了编程和软件工程。诸如 GitHub Copilot X 等 AI 编程助手实现了对话式编程，缩小了人类意图与代码生成之间的差距。然而，先前文献指出了一个关键挑战——在一系列自然语言表述之后，用户对系统理解的心智模型与 AI 系统的实际理解之间存在差距。为了解决这一问题，我们提出了 Programming with Representations（PwR），一种利用表示以自然语言将系统的理解反馈给用户的方法。我们开展了一项以任务为中心的实验室研究，邀请了 14 位编程水平各异的用户参与，结果发现表示显著提升了可理解性，并在参与者中建立了一种自主感。专家程序员利用表示进行验证，而中级程序员则从确认中受益。基于 LLM 的自然语言开发与表示相结合，有望变革软件开发，使其更加易于使用且更加高效。
英文摘要：
Large Language Models (LLMs) have revolutionized programming and software engineering. AI programming assistants such as GitHub Copilot X enable conversational programming, narrowing the gap between human intent and code generation. However, prior literature has identified a key challenge--there is a gap between user's mental model of the system's understanding after a sequence of natural language utterances, and the AI system's actual understanding. To address this, we introduce Programming with Representations (PwR), an approach that uses representations to convey the system's understanding back to the user in natural language. We conducted an in-lab task-centered study with 14 users of varying programming proficiency and found that representations significantly improve understandability, and instilled a sense of agency among our participants. Expert programmers use them for verification, while intermediate programmers benefit from confirmation. Natural language-based development with LLMs, coupled with representations, promises to transform software development, making it more accessible and efficient.
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。
