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Since its launch in November 2022, ChatGPT has gained popularity among users,especially programmers who use it as a tool to solve development problems.However, while offering a practical solution to programming problems, ChatGPTshould be mainly used as a supporting tool (e.g., in software education) ratherthan as a replacement for the human being. Thus, detecting automaticallygenerated source code by ChatGPT is necessary, and tools for identifyingAI-generated content may need to be adapted to work effectively with sourcecode. This paper presents an empirical study to investigate the feasibility ofautomated identification of AI-generated code snippets, and the factors thatinfluence this ability. To this end, we propose a novel approach calledGPTSniffer, which builds on top of CodeBERT to detect source code written byAI. The results show that GPTSniffer can accurately classify whether code ishuman-written or AI-generated, and outperforms two baselines, GPTZero andOpenAI Text Classifier. Also, the study shows how similar training data or aclassification context with paired snippets helps to boost classificationperformances.

Is this Snippet Written by ChatGPT? An Empirical Study with a CodeBERT-Based Classifier