msl9810 / learning-program-representation

Learning Program

Semantics with Code Representations: An Empirical Study

This repository contains the code and data in our paper, "Learning Program Semantics with Code Representations: An Empirical Study" published in SANER'2022. It includes POJ104Clone and POJ dataset.

• Clone Detection - Pairwise Clone Detection
• Code Classification - Classify Code in their respective label
• Vulnerability Detection - See Devign

Dataset

I had uploaded the dataset to google drive. You can download it here

Train

You can train the model with the sample command:

python3 -u /home/jingkai/projects/cit/train.py --config_path ./ymls/clone_detection/tfidf/naivebayes.yml

Please look into ./ymls/<tasks>/*.yml for setting the configurations.

Citation

If you find this repository useful in your research, please consider citing it:

@inproceedings{siow2022learning,
  title={Learning Program Semantics with Code Representations: An Empirical Study},
  author={Jing Kai, Siow and Shangqing, Liu and Xiaofei, Xie and Guozhu, Meng and Yang, Liu},
  booktitle={Proceedings of the 29th IEEE International Conference onSoftware Analysis, Evolution and Reengineering},
  year={2022}
}