CGLNaming

This repository contains data and code for our ICECCS 2022 paper "Combining Global and Local Representations of Source Code for Method Naming".

Data:

We already put the processed data in the compressed package. You can obtain the code that we parse Java code from here

You can also get the processed data from google drive.

Training/Testing Models:

We only provide our CD model code here. Other models can be easily obtained by modifying this basis.

$ cd scripts/DATASET_NAME

where, choices for DATASET_NAME are ["java"]

To train/evealuate the GTrans model, run:

$ bash transformer-js.sh 0 code2jdoc

where, 0 means GPU_ID.

Running experiments on CPU/GPU/Multi-GPU

• If GPU_ID is set to -1, CPU will be used.
• If GPU_ID is set to one specific number, only one GPU will be used.
• If GPU_ID is set to multiple numbers (e.g., 0,1,2), then parallel computing will be used.

Generated log files

While training and evaluating the models, a list of files are generated inside a DATASET_NAME-tmp directory. The files are as follows.

• MODEL_NAME.mdl
  • Model file containing the parameters of the best model.
• MODEL_NAME.mdl.checkpoint
  • A model checkpoint, in case if we need to restart the training.
• MODEL_NAME.txt
  • Log file for training.
• MODEL_NAME.json
  • The predictions and gold references are dumped during validation.
• MODEL_NAME_test.txt
  • Log file for evaluation (greedy).
• MODEL_NAME_test.json
  • The predictions and gold references are dumped during evaluation (greedy).

Acknowledgement

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