metadata

license: apache-2.0

Dataset Card for Dataset Name

Dataset Description

Repository: https://github.com/amazon-science/recode/tree/main
Paper: https://arxiv.org/abs/2212.10264

Dataset Summary

The Recode benchmark proposes to apply code and natural language transformations to code-generation benchmarks to evaluate the robustness of code-generation models. This dataset contains the perturbed version of HumanEval that they released. It was automatically generated from the HumanEval dataset.

Subsets

There are four transformation categories that form the subsets of this dataset: func_name, nlaugmenter, natgen and format.

Languages

The programming problems are written in Python and contains docstrings and comments in English.

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

task_id: ID of the original HumanEval example
prompt: the perturbed prompt
entry_point: entry point for test
canonical_solution: solution for the problem in the prompt
test: contains function to test generated code for correctness
seed: seed of the perturbed prompt
perturbation_name: name of the perturbation
partial: partial solution to the problem. This field is only present for transformation categories that affect a partial solution: natgen and format.

Data Splits

The dataset only has a test split.

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@article{wang2022recode,
  title={ReCode: Robustness Evaluation of Code Generation Models},
  author={Wang, Shiqi and Li, Zheng and Qian, Haifeng and Yang, Chenghao and Wang, Zijian and Shang, Mingyue and Kumar, Varun and Tan, Samson and Ray, Baishakhi and Bhatia, Parminder and others},
  journal={arXiv preprint arXiv:2212.10264},
  year={2022}
}

Contributions

[More Information Needed]