---
configs:
- config_name: repo_split_balanced
  data_files:
  - split: train
    path: repo_split_balanced/train-*
  - split: validation
    path: repo_split_balanced/validation-*
  - split: test
    path: repo_split_balanced/test-*
- config_name: random_shuffle
  data_files:
  - split: train
    path: random_shuffle/train-*
  - split: validation
    path: random_shuffle/validation-*
  - split: test
    path: random_shuffle/test-*
task_categories:
- text-retrieval
language:
- en
- zh
---

# ArkTS-CodeSearch-Pro: A Cleaned and License-Aware ArkTS Dataset

[Evaluation Code](https://anonymous.4open.science/r/retrieval_eval-3FA8) |[Dataset processing Code](https://anonymous.4open.science/r/arkts-codesearch-2140) | [Fine-Tuned Model](https://anonymous-hf.com/a/9ikxxiq5fvkp/) 

This dataset contains function-level information from ArkTS (`.ets`) projects, including function source code, normalized docstrings, AST representations, and repository metadata. It is intended for code retrieval, code understanding, and AST-based research.



![image](https://cdn-uploads.huggingface.co/production/uploads/653f6ec4e8ed050cb35d2dc9/QZamj21C9LPB8DkR30hWR.png)

Compared with the original release, this Pro version provides the following improvements:

- **License-aware collection**: only repositories with locally identified open-source licenses are included. The supported license families include Apache-2.0, MIT, BSD, GPL, LGPL, and MPL.
- **Repository quality filtering**: only repositories with more than zero stars are retained.
- **Comment normalization**: comment delimiters such as `//`, `/*`, `*/`, and leading `*` are removed from docstrings.
- **Noise removal**: copyright/license headers, code-only comments, and cleaned docstrings shorter than five characters are excluded.
- **Stronger deduplication**: complete function texts and cleaned queries are globally unique.
- **Repository-isolated evaluation**: `repo_split_balanced` groups repositories by normalized `nwo` across GitHub, Gitee, and GitCode, preventing the same cross-platform repository name from appearing in multiple splits.
- **Stable split sizes**: both configurations are approximately 80%/10%/10%.

The final dataset contains **31,164 examples**, all extracted from `.ets` files. The `sha` column was removed because commit SHA metadata was unavailable for most source repositories; `function_sha` is retained as a function-content identifier.

## Dataset Structure

The dataset provides two configurations:

- `repo_split_balanced`: repository-level split with normalized `nwo` repository isolation. It contains 24,932 training, 3,116 validation, and 3,116 test examples.
- `random_shuffle`: globally shuffled split with seed `20260714`. It contains 24,931 training, 3,117 validation, and 3,116 test examples.

## Features / Columns

| Field | Type | Description |
|-------|------|-------------|
| `nwo` | string | Repository name |
| `path` | string | `.ets` file path |
| `language` | string | Programming language (`arkts`) |
| `identifier` | string | Function identifier / name |
| `docstring` | string | Cleaned, normalized function documentation |
| `function` | string | Original function source code |
| `ast_function` | string | AST representation generated with tree-sitter-arkts |
| `obf_function` | string | Legacy obfuscated-function field; currently equal to `function` |
| `url` | string | Repository or source-code URL when available |
| `function_sha` | string | Function-content identifier |
| `source` | string | Code source platform (GitHub / Gitee / GitCode) |

## Usage

```python
from datasets import load_dataset

# Repository-isolated configuration
dataset = load_dataset(
    "XXX/arkts-code-docstring-for-anonymous",
    name="repo_split_balanced",
)

# Random-shuffle configuration
random_dataset = load_dataset(
    "XXX/arkts-code-docstring-for-anonymous",
    name="random_shuffle",
)

print(dataset["train"][0])
print(dataset["train"].features)
```

### License Configuration

All included repositories were selected from locally identified open-source repositories. Users must still comply with the individual upstream repository licenses and attribution requirements. This dataset does not grant a new license over the source code.