Transformers for variable misuse, function naming and code completion tasks

The official PyTorch implementation of:

• Empirical Study of Transformers for Source Code [arxiv] (accepted to ESEC/FSE'21)
• A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code [arxiv] (accepted to NAACL'21)

The repository also contains code for resplitting Python150k and JavaScript150k datasets (with splitting by repository, removing duplicates and the redistributable version of Py150k).

Repository structure

• data_utils: scripts for downloading Python150k and JavaScript150k datasets and obtaining new train / val / test splits (with splitting by repository, removing duplicates and the redistributable version of Py150k)
• vm_fn: code for Variable Misuse (VM) and Function Naming (FN) tasks (additional preprocessing, models, training etc)
• cc: code for Code Completion (CC) task (additional preprocessing, models, training etc)

See README in each directory for details.

Run

The code was tested on a system with Linux 3.10.0. Experiments were run using a Tesla V100 GPU. Required libraries are listed in requirments.txt in VM_FN and CC directories. The implementation is based on PyTorch>=1.5.

Running experiments:

1. Download and resplit data, see data_utils for details;
2. Preprocess data for a task you are interested in (VM, FN or CC), see vm_fn or cc for details;
3. Run the experiment you are interested in, see vm_fn or cc for details.

Attribution

Parts of this code are based on the following repositories:

• A Transformer-based Approach for Source Code Summarization
• Code Completion by Feeding Trees to Transformers
• A redistributable subset of the ETH Py150 corpus
• Deduplication index for big code datasets
• OpenNMT
• DrQA

Citation

If you found this code useful, please cite our papers

@misc{chirkova2020empirical,
      title={Empirical Study of Transformers for Source Code}, 
      author={Nadezhda Chirkova and Sergey Troshin},
      year={2020},
      eprint={2010.07987},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@inproceedings{chirkova2020simple,
      title={A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code}, 
      author={Nadezhda Chirkova and Sergey Troshin},
      booktitle={North American Chapter of the Association for Computational Linguistics}
      year={2021}, 
}