Quality Assessment of ChatGPT Generated Code and their Use by Developers

Abstract

The release of large language models (LLMs) like ChatGPT has revolutionized software development. Prior works explored ChatGPT's generated response quality, the effectiveness of different prompting techniques, its performance in programming contests, etc. However, there is limited information regarding the practical usage of ChatGPT by software developers. This data mining challenge focuses on DevGPT, a curated dataset of developer-ChatGPT conversations encompassing prompts with ChatGPT's responses, including code snippets. Our paper leverages this dataset to investigate (RQ1) whether ChatGPT generates Python & Java code with quality issues; (RQ2) whether ChatGPT-generated code is merged into a repository, and, if it does, to what extent developers change them; and (RQ3) what are the main use cases for ChatGPT besides code generation. We found that ChatGPT-generated code suffers from using undefined/unused variables and improper documentation. They also have security issues related to improper resources and exception management. Our results show that ChatGPT-generated codes are hardly merged, and they are significantly modified before merging. Based on an analysis of developers' discussions and the developer-ChatGPT chats, we found that developers use ChatGPT for every stage of software development and leverage it to learn about new frameworks and development kits.

Ähnliche Arbeiten