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Artificial Intelligence Generated Content (AIGC) has garnered considerable attention for its impressive performance, with ChatGPT emerging as a leading AIGC model that produces high-quality responses across various applications, including software development and maintenance. Despite its potential, the misuse of ChatGPT poses significant concerns, especially in education and safetycritical domains. Numerous AIGC detectors have been developed and evaluated on natural language data. However, their performance on code-related content generated by ChatGPT remains unexplored. To fill this gap, in this paper, we present the first empirical study on evaluating existing AIGC detectors in the software domain. We created a comprehensive dataset including 492.5K samples comprising code-related content produced by ChatGPT, encompassing popular software activities like Q but generalization remains a challenge. The human evaluation reveals that detection by humans is quite challenging.
Wang et al. (Tue,) studied this question.