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Technical question and answering (Q&A) sites such as Stack Overflow havebecome an important source for software developers to seek knowledge. However,code snippets on Q&A sites are usually uncompilable and semantically incompletefor compilation due to unresolved types and missing dependent libraries, whichraises the obstacle for users to reuse or analyze Q&A code snippets. Priorapproaches either are not designed for synthesizing compilable code or sufferfrom a low compilation success rate. To address this problem, we propose ZS4C,a lightweight approach to perform zero-shot synthesis of compilable code fromincomplete code snippets using Large Language Model (LLM). ZS4C operates in twostages. In the first stage, ZS4C utilizes an LLM, i.e., ChatGPT, to identifymissing import statements for a given code snippet, leveraging our designedtask-specific prompt template. In the second stage, ZS4C fixes compilationerrors caused by incorrect import statements and syntax errors throughcollaborative work between ChatGPT and a compiler. We thoroughly evaluated ZS4Con 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 a39.3% improvement. On average, ZS4C can infer more accurate import statementsthan SnR, with an improvement of 6.6% in the F1.

ZS4C: Zero-Shot Synthesis of Compilable Code for Incomplete Code Snippets using ChatGPT