# CI-REPAIR-BENCH

## Overview
**CI-REPAIR-BENCH** is a benchmark dataset for research on Continuous Integration (CI) failures and automated repair in Python repositories.

The dataset contains **567 CI failure instances** collected from **105 real-world GitHub repositories**, all written in **Python**.  
Each instance captures a CI workflow failure, its logs, the corresponding code diff, and repository-level metadata.

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## Dataset Statistics
- Programming language: Python
- Number of repositories: 105
- Number of CI instances: 567
- Domain: Continuous Integration
- Format: Parquet
- Size category: 10M < n < 100M
- Metadata language: English

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## Data Description
Each row in the dataset represents a CI failure scenario extracted from a GitHub repository.

### Main fields
- `language`: Programming language of the repository
- `id`: Unique identifier of the CI instance
- `repo_owner`: GitHub repository owner
- `repo_name`: GitHub repository name
- `head_branch`: Branch associated with the CI run
- `workflow_name`: Name of the CI workflow
- `workflow_filename`: CI workflow file name
- `workflow_path`: Path to the workflow file
- `sha_fail`: Commit SHA where the CI failed
- `sha_success`: Commit SHA where the CI succeeded
- `workflow`: Full CI workflow configuration
- `logs`: CI execution logs capturing failure details
- `diff`: Code diff between failing and fixed versions
- `changed_files`: List of files modified in the fix
- `commit_link`: Link to the failing commit
- `error_type`: Categorized CI error type

### Example error types
- Code Linting  
- Code Formatting  
- Dependency Issues  
- Package Installation Error  
- Test Failure  
- Runtime Error  
- Syntax Error  
- Configuration Error  
- Environment Error  
- Assertion Error  
- Documentation / Docstring Error  
- Type Checking Error  

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## Intended Use
This dataset can be used for:
- CI Log Analysis
- Automated Program Repair
- Learning from CI logs and diffs
- Benchmarking CI repair tools
- Empirical software engineering research

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## How to Use the Dataset

### Download from Hugging Face Hub
```python
from huggingface_hub import hf_hub_download

dataset_path = hf_hub_download(
    repo_id="ci-benchmark-user/ci-repair-bench",
    filename="ci_repair_dataset.parquet",
    repo_type="dataset",
    token=config.get("HUGGINGFACE_TOKEN")
)