File size: 2,568 Bytes
25ae426 2a3308d 09e53b0 2a3308d 09e53b0 25ae426 d232418 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | ---
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) |