NaturalCC: an Open-Source Toolkit for Code Intelligence
2022 IEEE/ACM 44th International Conference on Software Engineering Companion Proceedings (ICSE-Companion)(2022)
Abstract
We present NaturalCC, an efficient and extensible open-source toolkit for machine-learning-based source code analysis (i.e., code intelligence). Using NaturalCC, researchers can conduct rapid prototyping, reproduce state-of-the-art models, and/or exercise their own algorithms. NaturalCC is built upon Fairseq and PyTorch, providing (1) a collection of code corpus with preprocessing scripts, (2) a modular and extensible framework that makes it easy to repro-duce and implement a code intelligence model, and (3) a benchmark of state-of-the-art models. Furthermore, we demonstrate the usability of our toolkit over a variety of tasks (e.g., code summarization, code retrieval, and code completion) through a graphical user interface. The website of this project is http://xcodemind.github.io, where the source code and demonstration video can be found.
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Key words
Code intelligence,deep learning,code representation,code embedding,open source,toolkit,benchmark
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