大语言模型代码生成提示指南：一项实证性特征描述 / Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization
1️⃣ 一句话总结
这项研究通过自动测试分析，总结出10条优化代码生成提示的实用指南（如明确输入输出、提供示例等），并发现开发者对这些指南的实际使用情况与主观认知存在差异，为软件开发和工具设计提供了参考。
arXiv 提交日期: 2026-01-19
Abstract - Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization
Large Language Models (LLMs) are nowadays extensively used for various types of software engineering tasks, primarily code generation. Previous research has shown how suitable prompt engineering could help developers in improving their code generation prompts. However, so far, there do not exist specific guidelines driving developers towards writing suitable prompts for code generation. In this work, we derive and evaluate development-specific prompt optimization guidelines. First, we use an iterative, test-driven approach to automatically refine code generation prompts, and we analyze the outcome of this process to identify prompt improvement items that lead to test passes. We use such elements to elicit 10 guidelines for prompt improvement, related to better specifying I/O, pre-post conditions, providing examples, various types of details, or clarifying ambiguities. We conduct an assessment with 50 practitioners, who report their usage of the elicited prompt improvement patterns, as well as their perceived usefulness, which does not always correspond to the actual usage before knowing our guidelines. Our results lead to implications not only for practitioners and educators, but also for those aimed at creating better LLM-aided software development tools.
源自 arXiv: 2601.13118