---
license: mit
language:
- en
size_categories:
- n<1K
dataset_info:
  features:
  - name: instance_id
    dtype: string
  - name: hints_text
    dtype: string
  - name: patch
    dtype: string
  - name: test_patch
    dtype: string
  - name: created_at
    dtype: string
  - name: problem_statement
    dtype: string
  - name: repo
    dtype: string
  - name: base_commit
    dtype: string
  - name: version
    dtype: string
  - name: PASS_TO_PASS
    sequence: string
  - name: FAIL_TO_PASS
    sequence: string
  splits:
  - name: test
    num_bytes: 6514121
    num_examples: 100
  download_size: 1523176
  dataset_size: 6514121
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---
# 🔧 Selected SWE-Gym Subset

A curated subset of 100 program repair instances from the [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym) dataset, selected for lightweight evaluation and rapid prototyping.

## 📦 Dataset Description

This dataset contains 100 program repair tasks selected from the full SWE-Gym benchmark. Each instance represents a realistic software bug scenario, including the following fields:

* `instance_id`: Unique identifier
* `repo`: GitHub repository
* `commit`: Bug-inducing commit hash
* `test_setup`: Test setup instructions
* `test_commands`: How to run the test
* `relevant_files`: Files to be considered
* `expected_output`: Expected behavior
* `language`: Programming language
* `difficulty`: (if available) Estimated difficulty
* `summary`: Natural language bug description

All instances are formatted in JSONL (`.jsonl`) for compatibility with LLM pipelines and benchmarking scripts.

## ✅ Usage

You can load the dataset using the `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("dcloud347/Selected_SWE-Gym")
print(dataset["train"])
```

## 💡 Motivation

Evaluating automatic program repair systems on the full SWE-Gym benchmark can be resource-intensive. This curated 100-instance subset enables:

* Fast debugging of repair pipelines
* Lightweight academic comparisons
* Evaluation of few-shot LLM repair models
* Quick iteration on toolchain design

## 📁 Dataset Structure

```
data.jsonl
├─ {"instance_id": ..., "repo": ..., "commit": ..., ...}
├─ ...
```

## 📜 License

This subset follows the same license as the original SWE-Gym dataset (MIT). Please credit the original authors when using this dataset in your research.

## 🙏 Acknowledgements

* Original dataset: [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym)