The official implemention of "Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting".
We refactored the implementation into a Jupyter notebook format, shown in nbs dirs.
Datas and Model checkpoints stored at: https://drive.google.com/drive/folders/1By7RYsaPvYVQFrAr5Oc7_4JBGeXS1euC?usp=sharing
Some key Jupyter Notebooks.
gather_all_baseline.ipynb
Collect the results of statistical baselines in a single run.
apps_dataset_prepro.ipynb, apps_process.ipynb, comments_remove.ipynb, data_collection.ipynb
Operations on the dataset.
simcse_data.ipynb, simcse_inference.ipynb
Operations for SimCSE (self-supervised contrastive learning).
impact_code_correctness.ipynb
Experimental study on code correctness.
impact_of_m.ipynb
Experimental study on rewriting number m.
vary_codelength.ipynb
Experimental study on the impact of code length.
vary_decoding_temperature.ipynb
Experimental study on the impact of decoding temperature.
vary_identifier_replace.ipynb
Experimental study on the impact of Revised Synthetic Code.
vllm_results_gather.ipynb
Summarize the experimental results of our method.