Technical question and answering (Q&A) sites such as Stack Overflow have become an important source for software developers to seek knowledge. However, code snippets on Q&A sites are usually uncompilable and semantically incomplete for compilation due to unresolved types and missing dependent libraries, which raises the obstacle for users to reuse or analyze Q&A code snippets. Prior approaches either are not designed for synthesizing compilable code or suffer from a low compilation success rate. To address this problem, we propose ZS4C, a lightweight approach to perform zero-shot synthesis of compilable code from incomplete code snippets using Large Language Model (LLM). ZS4C operates in two stages. In the first stage, ZS4C utilizes an LLM, i.e., ChatGPT, to identify missing import statements for a given code snippet, leveraging our designed task-specific prompt template. In the second stage, ZS4C fixes compilation errors caused by incorrect import statements and syntax errors through collaborative work between ChatGPT and a compiler. We thoroughly evaluated ZS4C on a widely used benchmark called StatType-SO against the SOTA approach SnR. Compared with SnR, ZS4C improves the compilation rate from 63% to 87.6%, with a 39.3% improvement. On average, ZS4C can infer more accurate import statements than SnR, with an improvement of 6.6% in the F1.

通过使用大型语言模型，本研究提出了ZS4C，一个轻量级的零-shot合成代码的方法，通过两个阶段的操作，可以从不完整的代码片段中合成可编译的代码，并在与现有方法SnR的比较中实现了显著的编译成功率提升和更准确的导入语句推断。

ZS4C：使用ChatGPT进行零-shot综合不完整代码片段的可编译代码生成