#!/usr/bin/env python3
"""
Bug Detection Benchmark for CodeReality-1T Dataset

This benchmark evaluates bug detection systems on deliberately noisy code data.
Analyzes commit pairs to identify potential bugs and fixes in real-world repositories.

Status: PLANNED - Framework scaffold for future implementation
"""

import json
import os
import re
from typing import Dict, List, Tuple, Any
from collections import defaultdict
import random

def load_dataset_sample(data_dir: str, sample_size: int = 500) -> List[Dict]:
    """
    Load sample of repositories with commit history for bug detection analysis.

    Args:
        data_dir: Path to CodeReality-1T unified dataset
        sample_size: Number of repositories to sample

    Returns:
        List of repository data with commit pairs
    """
    # TODO: Implement repository loading with commit history
    # Focus on repositories with:
    # - Multiple commits with bug-fix indicators
    # - Before/after code changes
    # - Issue tracking data
    print(f"Loading {sample_size} repositories for bug detection analysis...")
    return []

def extract_bug_fix_patterns(repositories: List[Dict]) -> List[Dict]:
    """
    Extract potential bug-fix commit pairs from repository history.

    Args:
        repositories: List of repository data

    Returns:
        List of bug-fix patterns with before/after code
    """
    # TODO: Implement bug-fix pattern extraction
    # Look for:
    # - Commit messages with "fix", "bug", "issue" keywords
    # - Code changes that add null checks, exception handling
    # - Revert patterns and subsequent fixes
    patterns = []

    bug_keywords = ["fix", "bug", "issue", "error", "crash", "null", "exception"]

    for repo in repositories:
        # Extract commit pairs where bug-fix indicators are present
        pass

    return patterns

def simple_bug_detector(code_before: str, code_after: str) -> Dict[str, Any]:
    """
    Simple rule-based bug detection for demonstration purposes.

    This is a baseline implementation - real bug detection would use
    sophisticated ML models, static analysis, or dynamic testing.

    Args:
        code_before: Code before the fix
        code_after: Code after the fix

    Returns:
        Detection results with confidence scores
    """
    # TODO: Implement simple pattern-based bug detection
    # Examples:
    # - Missing null checks
    # - Array bounds issues
    # - Resource leaks
    # - Logic errors

    results = {
        "bug_detected": False,
        "bug_type": "unknown",
        "confidence": 0.0,
        "patterns_matched": [],
        "fix_applied": False
    }

    # Simple pattern matching for demonstration
    null_check_added = "!= null" in code_after and "!= null" not in code_before
    bounds_check_added = "length" in code_after and "length" not in code_before

    if null_check_added:
        results["bug_detected"] = True
        results["bug_type"] = "null_pointer"
        results["confidence"] = 0.7
        results["patterns_matched"].append("null_check_added")
        results["fix_applied"] = True

    return results

def evaluate_bug_detection(bug_patterns: List[Dict]) -> Dict[str, Any]:
    """
    Evaluate bug detection accuracy on commit pairs.

    Args:
        bug_patterns: List of bug-fix patterns

    Returns:
        Evaluation metrics including precision, recall, F1
    """
    # TODO: Implement comprehensive evaluation
    # Metrics:
    # - True positive rate (bugs correctly identified)
    # - False positive rate (false alarms)
    # - Precision, Recall, F1 score
    # - Bug type classification accuracy

    total_patterns = len(bug_patterns)
    detected_bugs = 0
    correct_detections = 0
    false_positives = 0

    for pattern in bug_patterns:
        # Apply simple bug detector
        result = simple_bug_detector(pattern.get("code_before", ""),
                                   pattern.get("code_after", ""))

        if result["bug_detected"]:
            detected_bugs += 1
            # In real scenario, would compare against ground truth
            # For demo, assume 60% accuracy
            if random.random() < 0.6:
                correct_detections += 1
            else:
                false_positives += 1

    precision = correct_detections / detected_bugs if detected_bugs > 0 else 0
    recall = correct_detections / total_patterns if total_patterns > 0 else 0
    f1_score = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0

    return {
        "total_patterns": total_patterns,
        "detected_bugs": detected_bugs,
        "correct_detections": correct_detections,
        "false_positives": false_positives,
        "precision": precision,
        "recall": recall,
        "f1_score": f1_score,
        "detection_rate": detected_bugs / total_patterns if total_patterns > 0 else 0
    }

def run_benchmark(repositories: List[Dict]) -> Dict[str, Any]:
    """
    Run complete bug detection benchmark.

    Args:
        repositories: List of repository data

    Returns:
        Complete benchmark results
    """
    print("Extracting bug-fix patterns...")
    bug_patterns = extract_bug_fix_patterns(repositories)

    print("Evaluating bug detection...")
    metrics = evaluate_bug_detection(bug_patterns)

    print("Analyzing bug types...")
    bug_type_distribution = defaultdict(int)
    for pattern in bug_patterns:
        bug_type = pattern.get("bug_type", "unknown")
        bug_type_distribution[bug_type] += 1

    return {
        "benchmark_info": {
            "name": "Bug Detection Benchmark",
            "dataset": "CodeReality-1T",
            "version": "1.0.0",
            "description": "Evaluates bug detection on commit pairs",
            "status": "PLANNED - Framework scaffold"
        },
        "dataset_stats": {
            "total_repositories": len(repositories),
            "total_bug_patterns": len(bug_patterns),
            "avg_patterns_per_repo": len(bug_patterns) / len(repositories) if repositories else 0
        },
        "detection_metrics": metrics,
        "bug_type_distribution": dict(bug_type_distribution),
        "insights": [
            "This is a planned benchmark - implementation needed",
            "Real bug detection requires sophisticated analysis",
            "CodeReality-1T provides rich commit history for training",
            "Noisy dataset challenges standard detection methods"
        ],
        "recommendations": [
            "Implement advanced static analysis tools",
            "Use ML models trained on commit patterns",
            "Validate with manual inspection of detected bugs",
            "Consider temporal patterns in bug introduction/fixing"
        ]
    }

def print_benchmark_results(results: Dict[str, Any]):
    """Print formatted benchmark results."""
    print("\n" + "="*60)
    print("BUG DETECTION BENCHMARK RESULTS")
    print("="*60)

    info = results["benchmark_info"]
    print(f"Benchmark: {info['name']}")
    print(f"Dataset: {info['dataset']}")
    print(f"Status: {info['status']}")
    print(f"Description: {info['description']}")

    print("\nDataset Statistics:")
    stats = results["dataset_stats"]
    print(f"  Total Repositories: {stats['total_repositories']}")
    print(f"  Bug Patterns Found: {stats['total_bug_patterns']}")
    print(f"  Avg Patterns/Repo: {stats['avg_patterns_per_repo']:.2f}")

    print("\nDetection Metrics:")
    metrics = results["detection_metrics"]
    print(f"  Precision: {metrics['precision']:.3f}")
    print(f"  Recall: {metrics['recall']:.3f}")
    print(f"  F1 Score: {metrics['f1_score']:.3f}")
    print(f"  Detection Rate: {metrics['detection_rate']:.3f}")

    print("\nBug Type Distribution:")
    for bug_type, count in results["bug_type_distribution"].items():
        print(f"  {bug_type}: {count}")

    print("\nKey Insights:")
    for insight in results["insights"]:
        print(f"  • {insight}")

    print("\nRecommendations:")
    for rec in results["recommendations"]:
        print(f"  • {rec}")

def main():
    """Run bug detection benchmark on CodeReality-1T dataset."""
    # Configuration
    data_dir = "/mnt/z/CodeReality_Final/unified_dataset"
    sample_size = 100  # Reduced for planning phase

    print("CodeReality-1T Bug Detection Benchmark")
    print("Status: PLANNED - Framework scaffold only")
    print(f"Data directory: {data_dir}")
    print(f"Sample size: {sample_size}")

    # Load dataset sample
    print("\nLoading dataset sample...")
    repositories = load_dataset_sample(data_dir, sample_size)

    if not repositories:
        print("No repositories loaded - using mock data for demonstration")
        # Create mock data for demonstration
        repositories = [{"name": f"mock_repo_{i}", "commits": []} for i in range(10)]

    # Run benchmark
    results = run_benchmark(repositories)

    # Print results
    print_benchmark_results(results)

    # Save results
    output_file = "bug_detection_results.json"
    with open(output_file, 'w') as f:
        json.dump(results, f, indent=2)

    print(f"\nResults saved to: {output_file}")
    print("Note: This is a framework scaffold - full implementation needed")

if __name__ == "__main__":
    main()