| | --- |
| | 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] |
