Large Language Models (LLMs) are rapidly transforming software engineering, with coding assistants embedded in an IDE becoming increasingly prevalent. While research has focused on improving the tools and understanding developer perceptions, a critical gap exists in understanding how developers actually use these tools in their daily workflows, and, crucially, where they struggle. This paper addresses part of this gap through a multi-phased investigation of developer interactions with an LLM-powered code editing and transformation feature, Transform Code, in an IDE widely used at Google. First, we analyze telemetry logs of the feature usage, revealing that frequent re-prompting can be an indicator of developer struggles with using Transform Code. Second, we conduct a qualitative analysis of unsatisfactory requests, identifying five key categories of information often missing from developer prompts. Finally, based on these findings, we propose and evaluate a tool, AutoPrompter, for automatically improving prompts by inferring missing information from the surrounding code context, leading to a 27% improvement in edit correctness on our test set.

本研究解决了开发人员在日常工作流程中与大型语言模型（LLM）进行代码编辑时的具体困难，填补了现有研究的空白。通过对开发人员与一个广泛使用的LLM驱动的代码编辑工具的互动进行多阶段调查，发现了开发人员在使用过程中常常需要重新提示的现象，并提出了一种名为AutoPrompter的工具，能够根据代码上下文自动改进提示，从而提高了编辑的正确性。 

提示大型语言模型进行代码编辑：挑战与解决方案