AI-Assisted Bug Detection in Open-Source Software

With the rapid development of internet technology, open-source software mirror sites have become indispensable tools for developers and tech enthusiasts, serving as crucial platforms for resource acquisition. We selected and tested two open-source applications by downloading them from the Tsinghua University Open Source Software Mirror Site and the Nanjing University Open Source Software Mirror Site. By implementing black-box testing strategies, we identified several bugs and design flaws in the software. Furthermore, we utilized large language models such as ChatGPT-3.5, Gemini, Kimi, Llama, ERNIE Bot, and Tongyi Qianwen to analyze the potential causes of these issues, exploring new approaches to AI-assisted software quality assurance. Through comparative analysis of feedback from multiple interactions with these large language models, we systematically evaluated their effectiveness in the field of software testing. This study provides empirical evidence for optimizing model applications and enhancing testing efficiency.