在IDE中实现结构化提示词管理，提升代码生成效率与复用性。
Prompt-with-Me: in-IDE Structured Prompt Management for LLM-Driven Software Engineering
- 基于四维分类体系自动识别提示词意图与场景
- 通过模板提取与敏感信息屏蔽提升提示质量
- 开发者实测反馈高效易用，显著减少重复工作
大型语言模型正在重塑软件工程，但实际开发中的提示词管理仍依赖非系统方法，影响可靠性、复用性及工业流程集成。我们提出Prompt-with-Me，一个嵌入开发环境的结构化提示词管理方案。系统采用包含意图、作者角色、开发周期阶段和提示类型在内的四维分类体系，自动对提示词进行分类。为提升复用性与质量，该工具可建议语言优化、屏蔽敏感信息，并从开发者提示库中提取可复用模板。对1108个真实提示词的分类研究显示，现代大模型可准确分类软件工程提示词。11名开发者的用户研究表明，系统具备高可用性（平均SUS=73）、低认知负荷（平均NASA-TLX=21），并显著提升了提示质量和效率，减少了重复劳动。最后，我们为下一代提示管理与维护工具提供了可操作的设计启示。
原文摘要 · Abstract (English)
Large Language Models are transforming software engineering, yet prompt management in practice remains ad hoc, hindering reliability, reuse, and integration into industrial workflows. We present Prompt-with-Me, a practical solution for structured prompt management embedded directly in the development environment. The system automatically classifies prompts using a four-dimensional taxonomy encompassing intent, author role, software development lifecycle stage, and prompt type. To enhance prompt reuse and quality, Prompt-with-Me suggests language refinements, masks sensitive information, and extracts reusable templates from a developer's prompt library. Our taxonomy study of 1108 real-world prompts demonstrates that modern LLMs can accurately classify software engineering prompts. Furthermore, our user study with 11 participants shows strong developer acceptance, with high usability (Mean SUS=73), low cognitive load (Mean NASA-TLX=21), and reported gains in prompt quality and efficiency through reduced repetitive effort. Lastly, we offer actionable insights for building the next generation of prompt management and maintenance tools for software engineering workflows.
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