发表机构
McGill University（麦吉尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过访谈和用户实验，设计并评估了工具dBlocks，通过块限定内容、上下文管理和内联验证，提升LLM辅助软件教程创作中写作者的信心并减少验证摩擦。
AI 中文摘要
随着大语言模型（LLM）的出现，创建软件教程现在涉及引导模型的输出，并将其塑造成连贯、准确的学习资源，然而现有的LLM工具为写作者提供的支持甚少。通过分析对技术写作者的访谈（N=17），我们确定了他们在组装和结构化多个LLM响应、策划模型使用的上下文以及验证生成内容方面的三个需求。我们设计了一个名为dBlocks的工具，具有以下功能：用于限定内容范围的块（blocks）、用于编辑上下文的上下文管理器（context manager），以及用于验证代码的内联执行（inline execution）。遵循以人为中心的设计方法，我们通过一项用户研究（N=5）迭代优化了设计。在一项受试者内实验研究（N=16）中，将dBlocks与参与者偏好的LLM辅助写作工作流程进行比较，参与者报告称，使用dBlocks制作的教程信心显著提高。此外，该工具减少了验证中的摩擦，写作者在起草时即验证代码，而不是推迟或跳过验证，并通过将工作范围限定为块，使每个教程部分及其LLM对话保持在一起，帮助他们避免搜索冗长的聊天记录。更广泛地说，我们的工作为在软件工程工作流程中支撑人机协作的工具提供了启示，并展示了以人为中心的设计如何指导LLM集成工具的开发。
英文摘要
With LLMs, creating software tutorials now involves steering the model's output and shaping it into a coherent, accurate learning resource, yet existing LLM tools offer writers little support for this work. By analyzing interviews with technical writers ($N=17$), we identify three requirements for how they assemble and structure multiple LLM responses, curate the context the model uses, and verify the generated content. We designed a tool called dBlocks with the following features: blocks to scope content, a context manager to edit context, and inline execution to verify code. Following a human-centered design method, we iteratively refined the design through a user study ($N=5$). In a within-subjects lab study ($N=16$) comparing dBlocks with participants' preferred workflows for LLM-assisted authoring, participants reported significantly higher confidence in the tutorials they produced with dBlocks. In addition, the tool reduced friction in verification, with writers verifying code as they drafted rather than deferring or skipping it, and helped them avoid searching long chat histories by scoping their work into blocks that kept each tutorial section and its LLM conversation together. More broadly, our work offers implications for tools that scaffold human-AI collaboration in SE workflows and shows how human-centered design can guide the development of LLM-integrated tools.
Comments27 pages, 8 figures, 5 tables. Under Review at TOSEM Special Issue on Human AI Collaboration in Software Engineering
