Developers’ Perspective on Trustworthiness of Code Generated by ChatGPT: Insights from Interviews

The emergence of ChatGPT as a tool for code generation has garnered significant attention from software developers. Nevertheless, the reliability of code produced by Large Language Models (LLMs) like ChatGPT remains insufficiently explored. This article delves into the realm of ChatGPT-generated code, aiming to investigate the perspectives of esteemed programmers and researchers through interviews. The consensus among interviewees highlights that code generated by ChatGPT often lack accuracy, necessitating manual debugging and substantial time investment, particularly when dealing with complex code structures. Through a comprehensive analysis of the interview findings, this article identifies five primary challenges inherent to ChatGPT’s code generation process. The core objective of this research is to engage in an exploration of ChatGPT’s code generation trustworthiness, drawing insights from interviews with experts. By facilitating insightful discussions, the research aims to pave the way for proposing impactful enhancements that bolster the reliability of ChatGPT’s code outputs. To enhance the performance and overall dependability of LLMs, the article presents seven potential solutions tailored to address these challenges.