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Exploring Representation-Level Augmentation for Code Search

This repo contains code for Exploring Representation-Level Augmentation for Code Search, accepted to EMNLP 2022. In this codebase we provide instructions for reproducing our results from the paper. We hope that this work can be useful for future research on representation-level augmentation.

Environment

conda create -n RACS python=3.6 -y
conda activate RACS
conda install pytorch-gpu=1.7.1 -y
pip install transformers==4.18.0 scikit-learn nltk==3.6.1 tensorboardX tqdm more_itertools pytrec_eval elasticsearch tree_sitter
conda install faiss-cpu -c pytorch

Data

We follow the preprocessing rule described in GraphCodeBERT. Since our codes are developed based on that repo, you could follow the instructions described in ./GraphCodeBERT/README.md.

CodeBERT

Here we provide an example of CodeBERT. The augmentaiton methods are described in models.py. These augmentation methods are orthogonal to any siamese networks, so you could insert these augmentation code snippets to other models to evaluate the performance.

To train CodeBERT with representation-level augmentation, run:

lang=python
python run_siamese_test.py \
--model_type roberta \
--do_train \
--do_eval \
--do_retrieval \
--evaluate_during_training \
--eval_all_checkpoints \
--data_dir ../data/${lang}/ \
--train_data_file train.jsonl \
--code_type code \
--max_seq_length 200 \
--per_gpu_train_batch_size 32 \
--per_gpu_retrieval_batch_size 100 \
--learning_rate 1e-6 \
--num_train_epochs 30 \
--gradient_accumulation_steps 1 \
--output_dir ./model/codebert-${lang}-ra \
--encoder_name_or_path microsoft/codebert-base \
--lang ${lang} \
--mix_time 5 \ 
--ra

After training, the model will be automatically tested on the test sets. For detailed configurations, please see the argument parser part in code.

Note: To turn off representation-level augmentation, simply remove --ra in the command line.

GraphCodeBERT

Here we provide an example of GraphCodeBERT. The augmentation codes are the same with CodeBERT.

To train GraphCodeBERT with representation-level augmentation, run:

lang=python
python run.py \
--output_dir=./saved_models/graphbert-${lang}-ra \
--config_name=microsoft/graphcodebert-base \
--model_name_or_path=microsoft/graphcodebert-base \
--tokenizer_name=microsoft/graphcodebert-base \
--lang=${lang} \
--do_train \
--do_eval \
--do_test \
--train_data_file=../data/${lang}/train.jsonl \
--eval_data_file=../data/${lang}/valid.jsonl \
--test_data_file=../data/${lang}/test.jsonl \
--codebase_file=../data/${lang}/codebase.jsonl \
--num_train_epochs 30 \
--code_length 256 \
--data_flow_length 64 \
--nl_length 128 \
--train_batch_size 32 \
--eval_batch_size 64 \
--learning_rate 2e-5 \
--seed 123456 \
--mix_time 5 \
--ra

After training, the model will be automatically tested on the test sets. For detailed configurations, please see the argument parser part in code.

Note: To turn off representation-level augmentation, simply remove --ra in the command line.

Citation

If you found this repository useful, please consider citing:

@inproceedings{li2022exploring,
  title={Exploring Representation-level Augmentation for Code Search},
  author={Li, Haochen and Miao, Chunyan and Leung, Cyril and Huang, Yanxian and Huang, Yuan and Zhang, Hongyu and Wang, Yanlin},
  booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
  pages={4924--4936},
  year={2022}
}

Acknowledgement

The implementation of this repo relies on resources from CoCLR, GraphCodeBERT. The code is implemented using PyTorch, We thank the original authors for their open-sourcing.