An Approach for Rapid Source Code Development Based on ChatGPT and Prompt Engineering

Abstract

Code generation stands as a powerful technique in modern software development, improving development efficiency, reducing errors, and fostering standardization and consistency. Recently, ChatGPT has exhibited immense potential in automatic code generation. However, existing researches on code generation lack guidance for practical software development process. In this study, we utilized ChatGPT to develop a web-based code generation platform consisting of key components: User Interface, Prompt Builder, and Backend Service. Specifically, Prompt Builder dynamically generated comprehensive prompts to enhance model generation performance. We conducted experiments on 2 datasets to evaluate the performance of code generation in our approach. through 8 widely used metrics. The results demonstrate that (1) our Prompt Builder is effective, resulting in a 65.06% improvement in the exact match (EM), a 38.45% improvement in Bilingual Evaluation Understudy (BLEU), a 15.70% improvement in CodeBLEU, and a 50.64% improvement in Pass@1. (2) In real development scenarios, 98.5% of test cases can be validated through manual validation, highlighting the genuine assistance provided by the ChatGPT-based code generation approach.

Keywords

• ChatGPT
• prompt engineering
• code generation
• software development
• large language models