We propose GypSum, a new deep learning model that learns hybrid representations using graph neural networks and a pre-trained programming and natural language model. GypSum uses two encoders to learn from the AST-based graph and the token sequence of source code, respectively, and modifies the encoder-decoder sublayer in the Transformer's decoder to fuse the representations.

This repos is developed based on the environment of:

Python 3.7

PyTorch 1.7.0

1. How to construct graph for python and java program?

   you can use the function get_graph_from_source from proprocess/java(python)_graph_construction.py After processing the code, save the data as pkl into data folder.

2. How to run?

   ./run $GPU_ID$ $DATASET$ $TASK$ For example, if you want to train java model from scrach with cuda:0, you can just run command ./run 0 java train

Gypsum-main ├─- README.md ├── c2nl │   ├── __init__.py │   ├── __pycache__ │   ├── config.py │   ├── decoders │   ├── encoders │   ├── eval │   ├── inputters │   ├── models │   ├── modules │   ├── objects │   ├── tokenizers │   ├── translator │   └── utils ├── config │   ├── general_config.yml │   ├── java_xxx_xxx.yml │   ├── ... ├── data │   ├── java │   └── python ├── evaluation │   ├── bleu │   ├── evaluate.py │   ├── meteor │   └── rouge ├── gypsum │   ├── __pycache__ │   ├── data │   ├── metor.ipynb │   ├── model.py │   ├── modules │   ├── predict.py │   ├── train.py │   └── utils ├── modules │   ├── __pycache__ │   └── attention_zoo.py ├── preprocess │   ├── generate_java_graph.ipynb │   ├── java_graph_construct.py │   ├── python_ast.ipynb │   └── python_graph.py └── run

We have uploaded our dataset on google drive: Dataset For Experiment

We borrowed and modified code from DrQA, OpenNMT. We would like to expresse our gratitdue for the authors of these repositeries.