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CONCORD_ISSTA23

Code for ISSTA'23 paper "CONCORD: Clone-aware Contrastive Learning for Source Code"

Environment Setup

conda create -n concord Python=3.8.12;
conda activate concord;

# install torch
conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge;

# install apex
git clone https://github.com/NVIDIA/apex.git;
cd apex/;
git checkout feae3851a5449e092202a1c692d01e0124f977e4;
pip install -v --disable-pip-version-check --no-cache-dir ./;
cd ../

# install pip packages
cd CONCORD_ISSTA23;
pip install -r requirements.txt;
export PYTHONPATH=$(pwd);

Model Weights and Data

Link

You could find the pre-trained weights of the main CONCORD model and task-specific data here:

  • DOI

Data Pre-processing

During CONCORD's pre-training, we need to align the syntax labels with code tokens, which requires the data pre-processing. To avoid the distribution shift, the task-specific fine-tuning data needs to do the same pre-processing with the following two steps:

  • Parse the source code with Tree-sitter and tokenize the sequence following the grammar of corresponding programming languages.
  • Sub-tokenize with the pre-trained BPE model.

You could refer to the following steps and customize your own data processing accordingly, or check out our data samples (finetune_data.zip) for the expected format of the pre-processed code.

Build Tree-sitter

cd data_processing;
bash build_tree_sitter.sh;

Example-1: Process CodeXGLUE Defects Detection Data

python process_code.py \
    --task_name cxg_vuldetect \
    --input_file <path_to_CodeXGLUE>/Code-Code/Defect-detection/dataset/test.jsonl \
    --output_file cxg_vd_test.csv \
    --spm_model ../vocab/multilingual_5k_repo_50k_vocab.model

Example-2: Process CodeXGLUE Clone Detection Data

python process_code.py \
    --task_name cxg_clone \
    --input_file <path_to_CodeXGLUE>/Code-Code/Clone-detection-POJ-104/dataset/test.jsonl \
    --output_file cxg_clone_test.jsonl \
    --spm_model ../vocab/multilingual_5k_repo_50k_vocab.model

Note that process_code.py script is just to illustrate the basic data processing steps using CodeXGLUE raw data; you may need to customize the script for your specific goal.