---
license: mit
task_categories:
- text-generation
tags:
- code
- dataset
size_categories:
- n<1K
language:
- en
pretty_name: CodeEval
---
license: apache-2.0
---
# Dataset Card for Object-Oriented Programming

## Dataset Description

- **Repository:** [GitHub Repository](https://github.com/alphadl/OOP-eval)
- **Paper:** [Object-Oriented Programming Evaluation Benchmark for LLMs](https://arxiv.org/abs/2401.06628)

### Dataset Summary

The OOP benchmark consists of 431 instances, and contains three difficulty levels: Simple-level OOP, Moderate-level OOP, and Difficult-level OOP.

### Supported Tasks and Leaderboards

### Languages

The Object-Oriented Programming problems are written in Python and contain English natural text in comments and docstrings.

## Dataset Structure

```python
from datasets import load_dataset
load_dataset("oop")

DatasetDict({
    test: Dataset({
        features: ['task_id', 'question', 'canonical_solution', 'test_list', 'test_function', 'entry_point', 'test_matching', 'test_match_function'],
        num_rows: 431
    })
})
```

### Data Instances

#### OOP benchmark
```
{
    'task_id': 'OOP/0',
    'question': 'First, write a **WDS** class using the Python language. Then, within the WDS class, create a public function called **without_duplicates** to implement finding the length of the longest substring in a given string **s** that does not contain any duplicate characters.',
    'test_function': 'def test_run(content1):\n    return WDS().without_duplicates(content1)',
    'test_list': [
        'assert candidate("abcabcbb")==3',
        'assert candidate("bbbbb")==1',
        'assert candidate("pwwkew")==3'],
    'entry_point': 'test_run',
    'test_matching': 'assert candidate([["class WDS", "def without_duplicates"]]) == True',
    'test_match_function': 'def matching_function(content):\n    def run_match(text):\n        for task in text:\n            if task not in str_content:\n                return False\n        return True\n    len_cont = len(content)\n    if len_cont==1 and run_match(content[0]) == True:\n        return True\n    elif (len_cont==2 and run_match(content[0]) == True) or (len_cont==2 and run_match(content[1]) == True):\n        return True\n    else:\n        return False'
}
```

### Data Fields

- `task_id`: identifier for the data sample
- `question`: description of programming task
- `test_function`: run function for the test
- 'test_list': list of tests to verify solution
- `entry_point`: entry point for test
- 'test_matching': list of tests to verify solution
- 'test_match_function': matching function for the test

### Data Splits

The OOP dataset only consists of a test split with 431 samples.

## Dataset Creation

See section 3.2 of original [paper](https://arxiv.org/abs/2401.06628).

### Citation Information
```
@inproceedings{wang2024oop,
      title={OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Models}, 
      author={Shuai Wang and Liang Ding and Li Shen and Yong Luo and Bo Du and Dacheng Tao},
      year={2024},
      booktitle={Findings of the Association for Computational Linguistics: ACL 2023},
      url={https://arxiv.org/abs/2401.06628}, 
}
```

### Contributions

Thanks to [@lvwerra](https://github.com/lvwerra) for adding this dataset.