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NaturalCC: A Toolkit to Naturalize the Source Code Corpus

Yao Wan, Yang He, Jian-Guo Zhang, Yulei Sui, Hai Jin, Guandong Xu, Caiming Xiong, Philip S. Yu
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
arXiv:2012.03225 [cs.SE], (6 Dec 2020)

@misc{wan2020naturalcc,

   title={NaturalCC: A Toolkit to Naturalize the Source Code Corpus},

   author={Yao Wan and Yang He and Jian-Guo Zhang and Yulei Sui and Hai Jin and Guandong Xu and Caiming Xiong and Philip S. Yu},

   year={2020},

   eprint={2012.03225},

   archivePrefix={arXiv},

   primaryClass={cs.SE}

}

We present NaturalCC, an efficient and extensible toolkit to bridge the gap between natural language and programming language, and facilitate the research on big code analysis. Using NaturalCC, researchers both from natural language or programming language communities can quickly and easily reproduce the state-of-the-art baselines and implement their approach. NaturalCC is built upon Fairseq and PyTorch, providing (1) an efficient computation with multi-GPU and mixed-precision data processing for fast model training, (2) a modular and extensible framework that makes it easy to reproduce or implement an approach for big code analysis, and (3) a command line interface and a graphical user interface to demonstrate each model’s performance. Currently, we have included several state-of-the-art baselines across different tasks (e.g., code completion, code comment generation, and code retrieval) for demonstration. The video of this demo is available.
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