benchmarks/README.md

# CodeReality-1T Evaluation Benchmarks

This directory contains demonstration benchmark scripts for evaluating models on the CodeReality-1T dataset.

## Available Benchmarks

### 1. License Detection Benchmark
**File**: `license_detection_benchmark.py`

**Purpose**: Evaluates automated license classification systems on deliberately noisy data.

**Features**:
- Rule-based feature extraction from repository content
- Simple classification model for demonstration
- Performance metrics on license detection accuracy
- Analysis of license distribution patterns

**Usage**:
```bash
cd /path/to/codereality-1t/eval/benchmarks
python3 license_detection_benchmark.py
```

**Expected Results**:
- Low accuracy due to deliberately noisy dataset (0% license detection by design)
- Demonstrates robustness testing for license detection systems
- Outputs detailed distribution analysis

### 2. Code Completion Benchmark
**File**: `code_completion_benchmark.py`

**Purpose**: Evaluates code completion models using Pass@k metrics on real-world noisy code.

**Features**:
- Function extraction from Python, JavaScript, Java files
- Simple rule-based completion model for demonstration
- Pass@1, Pass@3, Pass@5 metric calculation
- Multi-language support with language-specific patterns

**Usage**:
```bash
cd /path/to/codereality-1t/eval/benchmarks
python3 code_completion_benchmark.py
```

**Expected Results**:
- Baseline performance metrics for comparison
- Language distribution analysis
- Quality scoring of completions

## Benchmark Characteristics

### Dataset Integration
- **Data Source**: Loads from `/mnt/z/CodeReality_Final/unified_dataset` by default
- **Sampling**: Uses random sampling for performance (configurable)
- **Formats**: Handles JSONL repository format from CodeReality-1T

### Evaluation Philosophy
- **Deliberately Noisy**: Tests model robustness on real-world messy data
- **Baseline Metrics**: Provides simple baselines for comparison (not production-ready)
- **Reproducible**: Deterministic evaluation with random seed control
- **Research Focus**: Results show challenges of noisy data, not competitive benchmarks

### Extensibility
- **Modular Design**: Easy to extend with new benchmarks
- **Configurable**: Sample sizes and evaluation criteria can be adjusted
- **Multiple Languages**: Framework supports cross-language evaluation

## Configuration

### Data Path Configuration
Update the `data_dir` variable in each script to point to your CodeReality-1T dataset:

```python
data_dir = "/path/to/your/codereality-1t/unified_dataset"
```

### Sample Size Adjustment
Modify sample sizes for performance tuning:

```python
sample_size = 500  # Adjust based on computational resources
```

## Output Files

Each benchmark generates JSON results files:
- `license_detection_results.json`
- `code_completion_results.json`

These contain detailed metrics and can be used for comparative analysis.

### Sample Results
Example results are available in `../results/`:
- `license_detection_sample_results.json` - Baseline license detection performance
- `code_completion_sample_results.json` - Baseline code completion metrics

These demonstrate expected performance on CodeReality-1T's deliberately noisy data.

## Requirements

### Python Dependencies
```bash
pip install json os re random typing collections
```

### System Requirements
- **Memory**: Minimum 4GB RAM for default sample sizes
- **Storage**: Access to CodeReality-1T dataset (3TB)
- **Compute**: Single-core sufficient for demonstration scripts

## Extending the Benchmarks

### Adding New Tasks
1. Create new Python file following naming convention: `{task}_benchmark.py`
2. Implement standard evaluation interface:
   ```python
   def load_dataset_sample(data_dir, sample_size)
   def run_benchmark(repositories)
   def print_benchmark_results(results)
   ```
3. Add task-specific evaluation metrics

### Supported Tasks
Current benchmarks cover:
- **License Detection**: Classification and compliance
- **Code Completion**: Generation and functional correctness

**Framework Scaffolds (PLANNED - Implementation Needed)**:
- [`bug_detection_benchmark.py`](bug_detection_benchmark.py) - Bug detection on commit pairs (scaffold only)
- [`cross_language_translation_benchmark.py`](cross_language_translation_benchmark.py) - Code translation across languages (scaffold only)

**Future Planned Benchmarks - Roadmap**:
- **v1.1.0 (Q1 2025)**: Complete bug detection and cross-language translation implementations
- **v1.2.0 (Q2 2025)**: Repository classification and domain detection benchmarks
- **v1.3.0 (Q3 2025)**: Build system analysis and validation frameworks
- **v2.0.0 (Q4 2025)**: Commit message generation and issue-to-code alignment benchmarks

**Community Priority**: Framework scaffolds ready for community implementation!

## Performance Notes

### Computational Complexity
- **License Detection**: O(n) where n = repository count
- **Code Completion**: O(n*m) where m = average functions per repository

### Optimization Tips
1. **Sampling**: Reduce sample_size for faster execution
2. **Filtering**: Pre-filter repositories by criteria
3. **Parallelization**: Use multiprocessing for large-scale evaluation
4. **Caching**: Cache extracted features for repeated runs

## Research Applications

### Model Development
- **Robustness Testing**: Test models on noisy, real-world data
- **Baseline Comparison**: Compare against simple rule-based systems
- **Cross-domain Evaluation**: Test generalization across domains

### Data Science Research
- **Curation Methods**: Develop better filtering techniques
- **Quality Metrics**: Research automated quality assessment
- **Bias Analysis**: Study representation bias in large datasets

## Citation

When using these benchmarks in research, please cite the CodeReality-1T dataset:

```bibtex
@misc{codereality2025,
  title={CodeReality-1T: A Large-Scale Deliberately Noisy Dataset for Robust Code Understanding},
  author={Vincenzo Gallo},
  year={2025},
  publisher={Hugging Face},
  howpublished={\\url{https://huggingface.co/vinsblack}},
  note={Version 1.0.0}
}
```

## Support

- **Issues**: https://github.com/vinsguru/codereality-1t/issues
- **Contact**: vincenzo.gallo77@hotmail.com
- **Documentation**: See main dataset README and documentation