project
Detecting AI-Generated Code
Comparing three method families ranging from traditional classifiers, to graph-based methods, to deep learning embedding-based classification.
This research project compares three approaches to detecting AI-generated code: traditional machine learning classifiers, syntax-tree and graph-based representations, and deep learning embedding-based classification.
The project also forms the basis of the paper From Syntax Trees to Embeddings: A Comparative Study of AI-Generated Code Detection. A separate publication record remains deferred until its publication metadata is confirmed.
The catalogued project record lists Python, PyTorch, PyTorch-Geometric, Tree-Sitter, and TensorBoard as its core stack.
Draft status
Full write-up in progress. This page currently summarizes the catalogued project record.