  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 at
https://www.youtube.com/watch?v=q4W5VSI-u3E&t=25s.
