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Distinguishing whether some code portions were implemented by humans or generated by a tool based on artificial intelligence has become hard. However, such a classification would be important as it could point developers towards some further validation for the produced code. Additionally, it holds significant importance in security, legal contexts, and educational settings, where upholding academic integrity is of utmost importance. We present EX-CODE, a novel and explainable model that leverages the probability of the occurrence of some tokens, within a code snippet, estimated according to a language model, to distinguish human-written from AI-generated code. EX-CODE has been evaluated on a heterogeneous real-world dataset and stands out for its ability to provide human-understandable explanations of its outcomes. It achieves this by uncovering the features that for a snippet of code make it classified as human-written code (or AI-generated code).
Bulla et al. (Fri,) studied this question.