run_evaluation.py

#!/usr/bin/env python3
"""
Kodezi Chronos Benchmark Evaluation Script

This script allows you to evaluate your own debugging models against the 
Multi-Random Retrieval (MRR) benchmark used in the Chronos paper.

Usage:
    python run_evaluation.py --model gpt-4 --dataset mrr-5k
    python run_evaluation.py --model your-model --api-endpoint http://localhost:8080

For detailed usage, see benchmarks/README.md
"""

import argparse
import json
import os
from typing import Dict, List, Any

def main():
    parser = argparse.ArgumentParser(description='Evaluate debugging models against Chronos benchmarks')
    parser.add_argument('--model', required=True, help='Model name (gpt-4, claude-3, or custom)')
    parser.add_argument('--dataset', default='mrr-5k', help='Dataset to use (mrr-5k, mrr-1k-sample)')
    parser.add_argument('--api-endpoint', help='API endpoint for custom models')
    parser.add_argument('--output', default='results.json', help='Output file for results')
    
    args = parser.parse_args()
    
    print(f"""
╔══════════════════════════════════════════════════════════╗
║        Kodezi Chronos Benchmark Evaluation Suite         ║
║                                                          ║
║  Evaluating: {args.model:<43} ║
║  Dataset: {args.dataset:<47} ║
║                                                          ║
║  This evaluation will test debugging capabilities on:    ║
║  • Null pointer exceptions                               ║
║  • Race conditions                                       ║
║  • Memory leaks                                          ║
║  • API misuse                                            ║
║  • Performance bugs                                      ║
║  • Logic errors                                          ║
║                                                          ║
║  Results will be compared against:                       ║
║  • Kodezi Chronos: 65.3% success rate                    ║
║  • GPT-4: 8.5% success rate                              ║
║  • Claude-3: 7.8% success rate                           ║
║                                                          ║
╚══════════════════════════════════════════════════════════╝
    """)
    
    # Placeholder for actual evaluation logic
    print("\n⚠️  Note: This is a demonstration script.")
    print("Full benchmark implementation will be available with the research release.")
    print("\nTo implement your own evaluation:")
    print("1. Load debugging scenarios from benchmarks/multi-random-retrieval/dataset/")
    print("2. For each scenario, attempt to fix the bug with your model")
    print("3. Validate the fix using the provided test cases")
    print("4. Calculate success rate and other metrics")
    print("\nSee benchmarks/multi-random-retrieval/MRR_IMPLEMENTATION.md for details.")

if __name__ == "__main__":
    main()