本文研究了大型语言模型（LLMs）能否从源代码中自动恢复用户故事，并探讨了提示设计对输出质量的影响。通过1750个不同复杂度的C++代码片段，评估了五种最先进的LLMs在六种提示策略下的表现，结果表明，所有模型在200行代码以下的F1分数平均为0.8。研究发现，一个示例能够使最小模型（8B）的性能匹配70B的大模型，而结构化推理的Chain-of-Thought仅对大型模型有轻微提升。

arXiv:2509.19587v1 Announce Type: cross Abstract: User stories are essential in agile development, yet often missing or outdated in legacy and poorly documented systems. We investigate whether large language models (LLMs) can automatically recover user stories directly from source code and how prompt design impacts output quality. Using 1,750 annotated C++ snippets of varying complexity, we evaluate five state-of-the-art LLMs across six prompting strategies. Results show that all models achieve, on average, an F1 score of 0.8 for code up to 200 NLOC. Our findings show that a single illustrative example enables the smallest model (8B) to match the performance of a much larger 70B model. In contrast, structured reasoning via Chain-of-Thought offers only marginal gains, primarily for larger models.