A Study of Web Code Generation Based on ChatGPT

With the rise of large language models (LLMs) such as ChatGPT in the field of code generation, these models have demonstrated impressive abilities in understanding code semantics and implementing complex functionalities, especially showing potential in web development scenarios. Developing web applications is a critical task widely used in interactive software systems across various fields. However, current automated web code generation still has limitations, often failing to cover complete front-end and back-end functionalities or achieve complex interactive logic. Based on this, this paper takes ChatGPT-4o as an example, constructing a comprehensive student management system to systematically analyze and evaluate its performance and applicability in generating front-end and back-end code. First, the paper outlines the system’s requirements analysis and module design. Then, it thoroughly documents the entire process of generating front-end and back-end code based on ChatGPT-4o. Through this process, the paper examines ChatGPT-4o’s performance in terms of code generation efficiency, functionality accuracy, and the level of human intervention required, analyzing its strengths and limitations with experimental data. The experimental results indicate that large models like ChatGPT significantly simplify code generation and accelerate the development process, yet still require human optimization when handling complex logic and interaction design.