pytorch=2.0.0;
    torchvision=0.15.1;
    torchaudio;
    datasets==1.16.1;
    transformers==4.21.1;
    tensorboard==2.12.2;
    tree-sitter==0.19.1;
    nltk=3.8.1;
    scipy=1.10.1;

Install the above requirements manully or execute the following script:

bash scripts/setup.sh

1. Download the dataset and models:

bash scripts/download.sh

2. Prepare the dataset for pre-training[optional]

bash scripts/prepare_dataset.sh

bash scripts/pre-train.sh -g [GPU_ID]

bash scripts/finetune_msggen.sh -g [GPU_ID] -l [cpp/csharp/java/javascript/python/fira]

The released checkpoint may performs better than stated in the paper. If the evaluation during fine-tuning takes too long, you can adjust the "--evaluate_sample_size" parameter. This parameter refers to the number of cases in the validation set during evaluation.

To evaluate the performance of a specific checkpoint, add the flag "-e" followed by the checkpoint path:

bash scripts/finetune_msggen.sh -g [GPU_ID] -l [cpp/csharp/java/javascript/python/fira] -e [path_to_model]

Note that if [path_to_model] is blank, this script will automatically evaluate our released checkpoint.

bash scripts/finetune_cup.sh -g [GPU_ID]

To evaluate a specific checkpoint like in Task 1, add the flag "-e" followed by the checkpoint path.

Additionally, we have released the the output result of CCT5 and baselines, which is stored at results/CommentUpdate. Execute the following script and assign the path_to_result_file to evaluate its effectiveness:

bash scripts/eval_cup_res.sh --filepath [path_to_result_file]

Fine-tune:

bash scripts/finetune_jitdp_SF.sh -g [GPU_ID]

Evaluate:

bash scripts/finetune_jitdp_SF.sh -g [GPU_ID] -e [path_to_model]

Fine-tune:

bash scripts/finetune_jitdp_SF_EF.sh -g [GPU_ID]

Evaluate:

bash scripts/finetune_jitdp_SF_EF.sh -g [GPU_ID] -e [path_to_model]

Fine-tune:

bash scripts/finetune_QE.sh -g [GPU_ID]

Evaluate:

bash scripts/finetune_QE.sh -g [GPU_ID] -e [path_to_model]

Fine-tune:

bash scripts/finetune_CodeReview.sh -g [GPU_ID]

Evaluate:

bash scripts/finetune_CodeReview.sh -g [GPU_ID] -e [path_to_model]

We reused some code from open-source repositories. We would like to extend our gratitude to the following repositories:

1. CodeT5
2. CodeBERT
3. NatGen

@inproceedings{lin2023cct5,
  title={CCT5: A Code-Change-Oriented Pre-Trained Model},
  author={Lin, Bo and Wang, Shangwen and Liu, Zhongxin and Liu, Yepang and Xia, Xin and Mao, Xiaoguang},
  booktitle={Proceedings of the 31th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
  year={2023}
}