The source code and datasets for the EnCoSum.

EnCoSum includes Java and Python datasets. If you want to train the model, you must download the datasets.

You can get it on: https://github.com/xing-hu/TL-CodeSum

You can get it on: https://github.com/EdinburghNLP/code-docstring-corpus

You can get it on: https://github.com/microsoft/CodeXGLUE/tree/main

EnCoSum uses the JDK compiler to parse java methods as ASTs, and the Treelib toolkit to prase Python functions as ASTs.

In datapre file, the get_java_ast.py generates ASTs for the Java dataset and get_python_ast.py generates ASTs for Python datasets. You can run the following command：

python3 source.code ast.json

The file get_adj.py gets ASTs with added control flow and data flow.

You can use BERT method to embed AST nodes, detail in here

pip install bert-serving-server  
pip install bert-serving-client

In Model file, the main.py enables to train the model. Train and test model:

Python3 main.py

The nlg-eval can be set up in the following way, detail in here.
Install Java 1.8.0 (or higher).
Install the Python dependencies, run:

pip install git+https://github.com/Maluuba/nlg-eval.git@master

If you want to run the model, you will need to install the following packages.

pytorch 1.7.1  
bert-serving-client 1.10.0  
bert-serving-server 1.10.0  
javalang 0.13.0  
nltk 3.5  
networkx 2.5  
scipy 1.1.0  
treelib 1.6.1