Project CodeNet is a large-scale dataset with approximately 14 million code samples, each of which is an intended solution to one of 4000 coding problems. The code samples are written in over 50 programming languages (although the dominant languages are C++, C, Python, and Java) and they are annotated with a rich set of information, such as its code size, memory footprint, cpu run time, and status, which indicates acceptance or error types. The dataset is accompanied by a repository, where we provide a set of tools to aggregate codes samples based on user criteria and to transform code samples into token sequences, simplified parse trees and other code graphs. A detailed discussion of Project CodeNet is available in this paper.

The rich annotation of Project CodeNet enables research in code search, code completion, code-code translation, and a myriad of other use cases. We also extracted several benchmarks in Python, Java and C++ to drive innovation in deep learning and machine learning models in code classification and code similarity.

Citation

 @inproceedings{puri2021codenet,
  author = {Ruchir Puri and David Kung and Geert Janssen and Wei Zhang and Giacomo Domeniconi and Vladmir Zolotov and Julian Dolby and Jie Chen and Mihir Choudhury and Lindsey Decker and Veronika Thost and Luca Buratti and Saurabh Pujar and Ulrich Finkler},
  title = {Project CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks},
  year = {2021},
 }