ManyTypes4Py: A Benchmark Python Dataset for Machine Learning-based Type Inference

by Mir, Amir M. and LatoŇ°kinas, Evaldas and Gousios, Georgios

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See the paper's associated code repository: saltudelft/many-types-4-py-dataset

Bibtex record

@inproceedings{MLG21,
  author = {Mir, Amir M. and LatoŇ°kinas, Evaldas and Gousios, Georgios},
  booktitle = {2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR)},
  title = {ManyTypes4Py: A Benchmark Python Dataset for Machine Learning-based Type Inference},
  month = may,
  year = {2021},
  volume = {1},
  pages = {585-589},
  doi = {10.1109/MSR52588.2021.00079},
  publisher = {IEEE Computer Society},
  address = {Los Alamitos, CA, USA},
  abstact = {
      
      In this paper, we present ManyTypes4Py, a large Python dataset for machine
      learning (ML)-based type inference. The dataset contains a total of 5,382
      Python projects with more than 869K type annotations. Duplicate source code
      files were removed to eliminate the negative effect of the duplication bias.
      To facilitate training and evaluation of ML models, the dataset was split
      into training, validation and test sets by files. To extract type
      information from abstract syntax trees (ASTs), a light- weight static
      analyzer pipeline is developed and accompanied with the dataset. Using this
      pipeline, the collected Python projects were analyzed and the results of the
      AST analysis were stored in JSON-formatted files. The ManyTypes4Py dataset
      is shared on zenodo and its tools are publicly available on GitHub.
  
    },
  url = {https://arxiv.org/pdf/2104.04706.pdf},
  github = {saltudelft/many-types-4-py-dataset}
}

The paper