CodeSage: Code Representation Learning At Scale

This repository contains the data and inference code of the ICLR 2024 paper "CodeSage: Code Representation Learning At Scale."

Work done by Dejiao Zhang*, Wasi Uddin Ahmad*, Ming Tan, Hantian Ding, Ramesh Nallapati, Dan Roth, Xiaofei Ma, Bing Xiang (* indicates equal contribution).

Overview

An overview of the key ingredients of CodeSage for code representation learning.

Environment Setup

conda create -n codesage_eval python=3.10
conda activate codesage_eval
pip install -r requirements.txt

Run Evaluation

Code-to-Code Search

See data preparation before running evaluation scripts.

bash scripts/run_code2code_search.sh MODEL_NAME SRC_LANG TGT_LANG

where

• MODEL_NAME = [codesage-small|codesage-base|codesage-large]
• SRC_LANG and TGT_LANG = [python|java|c|c++|csharp|ruby|php|go|javascript|typescript]

Text-to-Code Search

See data preparation before running evaluation scripts.

bash scripts/run_code2code_search.sh MODEL_NAME SRC_LANG TGT_LANG

where

• MODEL_NAME = [codesage-small|codesage-base|codesage-large]
• SRC_LANG and TGT_LANG = [cosqa|advTest|csn]

Code Classification

# clone detection
bash scripts/run_clone_detection.sh
# complexity prediction
bash scripts/run_complexity_prediction.sh
# defect prediction
bash scripts/run_defect_prediction.sh
# runtime error prediction
bash scripts/run_runtime_error_prediction.sh

Benchmark

Wanna compare CodeSage against the latest embedding model? Check out our code for benchmarking

Citation

@inproceedings{
    zhang2024codesage,
    title={CodeSage: Code Representation Learning At Scale},
    author={Dejiao Zhang* and Wasi Ahmad* and Ming Tan and Hantian Ding and Ramesh Nallapati and Dan Roth and Xiaofei Ma and Bing Xiang},
    booktitle={The Twelfth International Conference on Learning Representations },
    year={2024},
    url={https://openreview.net/forum?id=vfzRRjumpX}
}

Contact

If you have any question regarding our paper or code, please feel free to start an issue or email Dejiao Zhang (dejiaoz@amazon.com) and Wasi Ahmad (wuahmad@amazon.com).

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.