DISCOREV: crosstask knowledge DIStillation for COde REView

This is the replication package accompanying our paper, Improving the Learning of Code Review Successive Tasks with Cross-Task Knowledge Distillation, that is published in the ACM International Conference on the Foundations of Software Engineering 2024 (FSE'24).

Overview

DISCOREV is a novel deep-learning architecture that employs cross-task knowledge distillation to address code review tasks. In our approach, we utilize a cascade of models to enhance both comment generation and code refinement models. The fine-tuning of the comment generation model is guided by the code refinement model, while the fine-tuning of the code refinement model is guided by the quality estimation model. We implement this guidance using two strategies: a feedback-based learning objective and an embedding alignment objective

This project is designed to be standalone and utilizes Docker for easy replication of results, as well as to facilitate the model's usability, modification, and execution.

Project structure

Dockerfile

The Dockerfile provides a containerized environment for seamless replication of results and simplified usage and modification of the model.

comment_generation

This folder contains the code to train DISCOREV on the comment generation task.

├── config.py               # defines and parses the configuration (i.e., list of parameters) of the program
├── dataset.py              # reads and pre-processes the dataset 
├── evaluation.py           # defines the evaluation metrics
├── evaluate_model1.py      # evaluation script of model 1
├── evaluate_model2.py      # evaluation script of model 2
├── models.py               # defines the architecture of DISCOREV model
├── smooth_bleu.py          # defines BLEU metrics
├── utils.py                # utils for model tratining and data pre-preprocessing 
├── main.py                 # main script for training DISCOREV model

code_refinement

This folder contains the code to train DISCOREV on the code refinement task. It has a similar structure to the comment_generation folder.
data

This folder should contain the data used by the models (i.e., training, test, and validation datasets). We use this dataset for our experiments.

Training

To facilitate the training of DISCOREV, it can be done inside a Docker container. Follow the steps below to build the Docker image and train DISCOREV.

Clone the DISCOREV repository:

git clone https://github.com/OussamaSghaier/DISCOREV.git

Navigate to the DISCOREV directory:
```
cd DISCOREV
```
Build the Docker image:
```
docker build -t discorev-image .
```
Create a docker container from the built image and connect to the running Docker container:
```
docker run -it --rm --gpus all -v /tmp/DISCOREV/data/:/app/data --name discorev-container discorev-image
```
/tmp/DISCOREV/data is the absolute path to the data/ folder. The above command should be run once (the first time) to create a container from the built image. For subsequent attempts, we should use the following commands to start and connect to the created docker container:
```
docker start discorev-container
```
To stop the container:
```
docker stop discorev-container
```
DISCOREV is now set up to be trained within a Docker container for your data, and you can easily start, connect, and stop the container as needed.

Train DISCOREV: Execute the following command to train DISCOREV

python main.py 
    --train_file ../data/ref-train.jsonl 
    --test_file ../data/ref-test.jsonl 
    --valid_file ../data/ref-valid.jsonl
    --model_name_or_path microsoft/codereviewer 
    --num_epochs 30
    --batch_size 32
    --output_dir ./output
    --save-steps 5000
    --eval-steps 5000
    --log-steps 500
    --seef 1234
    --max_source_length 512 \
    --max_target_length 128 \
    --learning_rate 3e-4 \
    --gradient_accumulation_steps 3 \

Citing

If you use this code or DISCOREV, please consider citing us.

@article{sghaier2024improving,
    title={Improving the Learning of Code Review Successive Tasks with Cross-Task Knowledge Distillation},
    author={Sghaier, Oussama Ben and Sahraoui, Houari},
    journal={arXiv preprint arXiv:2402.02063},
    year={2024}
}

Contact us

We value your feedback and are here to assist you with any questions you may have regarding our paper. Please feel free to reach out to us:

Email: oussama.ben.sghaier@umontreal.ca
Website: https://oussamasghaier.github.io/

OussamaSghaier / DISCOREV

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