Requirements

• Python 3.7
• PyTorch 1.4
• torchtext 0.4
• CUDA & CuDNN to run on GPU (recommended)
• javalang 0.13
• pyastyle 1.1.5
• tqdm 4.47
• numpy 1.18.1
• NLTK 3.5
• py-rouge 1.1

Data

The original data we used is from https://github.com/xing-hu/TL-CodeSum and https://github.com/xing-hu/EMSE-DeepCom. This repository only contains a tiny dataset to verify whether the scripts work. Be aware that this dataset is not big enough to properly train a seq2seq model.

If you want to try some new data (in Java), simply reference and replace raw_data.json in the data folder. The code to shuffle and remove the duplicate samples is not included so you have to manually do this if required.

Parsing & Preprocessing

• First parse the codes into ASTs and then split them into statement subtrees. Codes that cannot be parsed or less than 2 statements (e.g., empty body) will be marked for discard.
  python Tree.py
• Filter & tokenize the codes and comments, and then split the codes by statement. A formatter (Artistic Style) is used here. Comments with less than 1 token after tokenization will be marked for discard. The remaining samples will be split into train/valid/test sets. Vocabularies will also be generated during this step.
  python Data.py

Build & Train the model

• The Train script will pretrain a pair of seq2seq models on token/node sequences and then use their encoders to initialize HACS-token/AST. After that, HACS-token/AST will be trained.
  python Train.py

Test & Evaluate the model

• Print the scores on test set and save the predictions.
  python Main.py

Change the Parameters

All parameters are defined in the beginning of the .py files. We will make the scripts easier to use in the future (e.g., by using argparse).