Datasets:

dcloud347
/

Selected_SWE-Gym

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 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:

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