---
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](https://huggingface.co/datasets/openai_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

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### 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]