ASTNN--A Novel Neural Source Code Representation based on Abstract Syntax Tree

This repository includes the code and experimental data in our paper entitled "A Novel Neural Source Code Representation based on Abstract Syntax Tree" published in ICSE'2019. It can be used to encode code fragments into supervised vectors for various source code related tasks. We have applied our neural source code representation to two common tasks: source code classification and code clone detection. It is also expected to be helpful in more tasks.

Requirements

• python 3.6
  
• pandas 0.20.3
  
• gensim 3.5.0
  
• scikit-learn 0.19.1
  
• pytorch 1.0.0
  (The version used in our paper is 0.3.1 and source code can be cloned by specifying the v1.0.0 tag if needed)
• pycparser 2.18
  
• javalang 0.11.0
  
• RAM 16GB or more
• GPU with CUDA support is also needed
• BATCH_SIZE should be configured based on the GPU memory size

How to install

Install all the dependent packages via pip:

$ pip install pandas==0.20.3 gensim==3.5.0 scikit-learn==0.19.1 pycparser==2.18 javalang==0.11.0

Install pytorch according to your environment, see https://pytorch.org/

Source Code Classification

1. cd astnn
2. run python pipeline.py to generate preprocessed data.
3. run python train.py for training and evaluation

Code Clone Detection

1. cd clone
2. run python pipeline.py --lang c or python pipeline.py --lang java to generate preprocessed data for the two datasets.
3. run python train.py --lang c to train on OJClone, python train.py --lang java on BigCLoneBench respectively.

How to use it on your own dataset

Please refer to the pkl files in the corresponding directories of the two tasks. These files can be loaded by pandas.

Citation

If you find this code useful in your research, please, consider citing our paper:

@inproceedings{zhang2019novel, title={A novel neural source code representation based on abstract syntax tree}, author={Zhang, Jian and Wang, Xu and Zhang, Hongyu and Sun, Hailong and Wang, Kaixuan and Liu, Xudong}, booktitle={Proceedings of the 41st International Conference on Software Engineering}, pages={783--794}, year={2019}, organization={IEEE Press} }