Senatus CV4code - Sourcecode Understanding via Visual Code Representations

This is the repository containing source code applying computer vision techniques for source code understanding.

Installation

Through pip

pip install -r requirements.txt

Dataset

The experiment of this repository is conducted on CodeNet avaialble from [https://github.com/IBM/Project_CodeNet].

Feature extraction and preparation

1. codepoint encoded image

This encodes source code file in a matrix with each element/position encoded by the character encoding order, e.g. configurable as ASCII or utf-8 (only first 3 bytes).

Extract ASCII codepoint images

python data/code2pixel.py Project_CodeNet/data $CODEPOINT_IMAGE_DIR --ascii-only --image-format npy --keep-source-structure

See other options

python data/code2pixel.py --help

2. Dataset descriptor preparation

After the images/matrices are extracted, one can prepare the dataset to be ready for experiments.

Prepare dataset descriptor with the extracted codepoint images above, holding out 7000 datapoints for validation and testing

python data/prepare_codenet.py Project_CodeNet/metadata/ $CODEPOINT_IMAGE_DIR $DATASET_DESCRIPTOR --val 7000 --test 70000

3. Vocabulary preparation (optional for token based transformer only)

Build vocab with minimum frequency of 2, from the train subset:

python3 build_vocab.py $DATASET_DESCRIPTOR Project_CodeNet/data/ --min_freq 2 --subset train --ascii_only 

Training

The main entry point of training is the train.py script and model hyperparameters and training recipes can almost entirely be specified in hparams/default.yml or other yaml config files. For example, to run a ConvVit model experiment from the above prepared dataset.

python train.py $EXP_TAG $DATASET_DESCRIPTOR --hparams hparams/default.yml

where

$EXP_TAG is the tag name of the experiment entry in the hparams yaml config file, e.g. conv_vit for ConvVit Model.

To cite our work:

@article{Shi2022CV4CodeSU,
  title={CV4Code: Sourcecode Understanding via Visual Code Representations},
  author={Ruibo Shi and Lili Tao and Rohan Saphal and Fran Silavong and Sean J. Moran},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.08585}
}