vinsblack/CodeReality

1#!/usr/bin/env python32"""3Cross-Language Translation Benchmark for CodeReality-1T Dataset4 5This benchmark evaluates cross-language code translation systems on deliberately noisy data.6Analyzes equivalent implementations across different programming languages.7 8Status: PLANNED - Framework scaffold for future implementation9"""10 11import json12import os13import re14from typing import Dict, List, Tuple, Any15from collections import defaultdict16import random17 18def load_dataset_sample(data_dir: str, sample_size: int = 500) -> List[Dict]:19    """20    Load sample of repositories with cross-language implementations.21 22    Args:23        data_dir: Path to CodeReality-1T unified dataset24        sample_size: Number of repositories to sample25 26    Returns:27        List of repository data with multi-language content28    """29    # TODO: Implement repository loading with cross-language focus30    # Target repositories with:31    # - Multiple programming languages32    # - Similar algorithms in different languages33    # - Bindings or wrapper implementations34    print(f"Loading {sample_size} multi-language repositories...")35    return []36 37def extract_language_pairs(repositories: List[Dict]) -> List[Dict]:38    """39    Extract equivalent code implementations across different languages.40 41    Args:42        repositories: List of repository data43 44    Returns:45        List of language pairs with equivalent functionality46    """47    # TODO: Implement language pair extraction48    # Look for:49    # - Similar function names across languages50    # - Algorithm implementations in multiple languages51    # - Test files that indicate equivalent functionality52    # - Documentation mentioning language equivalence53 54    language_pairs = []55 56    common_pairs = [57        ("python", "javascript"),58        ("java", "c++"),59        ("python", "java"),60        ("javascript", "typescript"),61        ("c", "c++"),62        ("python", "go"),63        ("java", "c#"),64        ("rust", "c++")65    ]66 67    for repo in repositories:68        # Extract code snippets that appear to implement similar functionality69        pass70 71    return language_pairs72 73def simple_translation_evaluator(source_code: str, target_code: str,74                                source_lang: str, target_lang: str) -> Dict[str, Any]:75    """76    Simple rule-based translation evaluation for demonstration purposes.77 78    This is a baseline implementation - real translation evaluation would use79    sophisticated semantic analysis, execution testing, or ML-based similarity.80 81    Args:82        source_code: Source language implementation83        target_code: Target language implementation84        source_lang: Source programming language85        target_lang: Target programming language86 87    Returns:88        Translation quality assessment89    """90    # TODO: Implement comprehensive translation evaluation91    # Methods:92    # - Structural similarity analysis93    # - API usage pattern matching94    # - Execution behavior comparison95    # - Performance characteristic analysis96 97    results = {98        "translation_quality": 0.0,99        "structural_similarity": 0.0,100        "semantic_equivalence": 0.0,101        "syntax_correctness": 0.0,102        "functionality_preserved": False,103        "common_patterns": [],104        "differences": []105    }106 107    # Simple pattern matching for demonstration108    # Count similar keywords, structure patterns109    source_tokens = re.findall(r'\w+', source_code.lower())110    target_tokens = re.findall(r'\w+', target_code.lower())111 112    # Language-agnostic concepts113    common_concepts = ["function", "class", "method", "variable", "loop", "condition"]114    source_concepts = [t for t in source_tokens if t in common_concepts]115    target_concepts = [t for t in target_tokens if t in common_concepts]116 117    if source_concepts and target_concepts:118        structural_sim = len(set(source_concepts) & set(target_concepts)) / len(set(source_concepts) | set(target_concepts))119        results["structural_similarity"] = structural_sim120 121    # Mock semantic equivalence (in real implementation, would use AST analysis)122    results["semantic_equivalence"] = random.uniform(0.3, 0.8)123    results["syntax_correctness"] = random.uniform(0.6, 0.95)124    results["translation_quality"] = (results["structural_similarity"] +125                                    results["semantic_equivalence"] +126                                    results["syntax_correctness"]) / 3127 128    results["functionality_preserved"] = results["translation_quality"] > 0.6129 130    return results131 132def evaluate_translation_pairs(language_pairs: List[Dict]) -> Dict[str, Any]:133    """134    Evaluate translation quality across language pairs.135 136    Args:137        language_pairs: List of cross-language implementation pairs138 139    Returns:140        Comprehensive translation evaluation metrics141    """142    # TODO: Implement comprehensive evaluation143    # Metrics:144    # - Translation accuracy by language pair145    # - Semantic preservation scores146    # - Syntax correctness rates147    # - Performance equivalence148 149    total_pairs = len(language_pairs)150    successful_translations = 0151    quality_scores = []152    language_pair_performance = defaultdict(list)153 154    for pair in language_pairs:155        source_code = pair.get("source_code", "")156        target_code = pair.get("target_code", "")157        source_lang = pair.get("source_language", "unknown")158        target_lang = pair.get("target_language", "unknown")159 160        result = simple_translation_evaluator(source_code, target_code,161                                            source_lang, target_lang)162 163        quality = result["translation_quality"]164        quality_scores.append(quality)165 166        if result["functionality_preserved"]:167            successful_translations += 1168 169        pair_key = f"{source_lang}->{target_lang}"170        language_pair_performance[pair_key].append(quality)171 172    # Calculate aggregate metrics173    avg_quality = sum(quality_scores) / len(quality_scores) if quality_scores else 0174    success_rate = successful_translations / total_pairs if total_pairs > 0 else 0175 176    # Language pair performance177    pair_stats = {}178    for pair_key, scores in language_pair_performance.items():179        pair_stats[pair_key] = {180            "count": len(scores),181            "avg_quality": sum(scores) / len(scores),182            "success_rate": sum(1 for s in scores if s > 0.6) / len(scores)183        }184 185    return {186        "total_pairs": total_pairs,187        "successful_translations": successful_translations,188        "success_rate": success_rate,189        "average_quality": avg_quality,190        "quality_distribution": {191            "excellent": sum(1 for q in quality_scores if q > 0.8),192            "good": sum(1 for q in quality_scores if 0.6 < q <= 0.8),193            "fair": sum(1 for q in quality_scores if 0.4 < q <= 0.6),194            "poor": sum(1 for q in quality_scores if q <= 0.4)195        },196        "language_pair_performance": pair_stats197    }198 199def run_benchmark(repositories: List[Dict]) -> Dict[str, Any]:200    """201    Run complete cross-language translation benchmark.202 203    Args:204        repositories: List of repository data205 206    Returns:207        Complete benchmark results208    """209    print("Extracting cross-language pairs...")210    language_pairs = extract_language_pairs(repositories)211 212    print("Evaluating translation quality...")213    metrics = evaluate_translation_pairs(language_pairs)214 215    print("Analyzing language coverage...")216    language_coverage = defaultdict(int)217    for pair in language_pairs:218        source_lang = pair.get("source_language", "unknown")219        target_lang = pair.get("target_language", "unknown")220        language_coverage[source_lang] += 1221        language_coverage[target_lang] += 1222 223    return {224        "benchmark_info": {225            "name": "Cross-Language Translation Benchmark",226            "dataset": "CodeReality-1T",227            "version": "1.0.0",228            "description": "Evaluates code translation across programming languages",229            "status": "PLANNED - Framework scaffold"230        },231        "dataset_stats": {232            "total_repositories": len(repositories),233            "total_language_pairs": len(language_pairs),234            "avg_pairs_per_repo": len(language_pairs) / len(repositories) if repositories else 0,235            "unique_languages": len(language_coverage)236        },237        "translation_metrics": metrics,238        "language_coverage": dict(language_coverage),239        "insights": [240            "This is a planned benchmark - implementation needed",241            "Cross-language translation requires semantic understanding",242            "CodeReality-1T provides diverse language combinations",243            "Noisy dataset challenges automated translation systems"244        ],245        "recommendations": [246            "Implement AST-based semantic analysis",247            "Use execution-based validation when possible",248            "Consider language-specific idiom preservation",249            "Validate with human expert review for complex cases"250        ]251    }252 253def print_benchmark_results(results: Dict[str, Any]):254    """Print formatted benchmark results."""255    print("\n" + "="*60)256    print("CROSS-LANGUAGE TRANSLATION BENCHMARK RESULTS")257    print("="*60)258 259    info = results["benchmark_info"]260    print(f"Benchmark: {info['name']}")261    print(f"Dataset: {info['dataset']}")262    print(f"Status: {info['status']}")263    print(f"Description: {info['description']}")264 265    print("\nDataset Statistics:")266    stats = results["dataset_stats"]267    print(f"  Total Repositories: {stats['total_repositories']}")268    print(f"  Language Pairs Found: {stats['total_language_pairs']}")269    print(f"  Avg Pairs/Repo: {stats['avg_pairs_per_repo']:.2f}")270    print(f"  Unique Languages: {stats['unique_languages']}")271 272    print("\nTranslation Metrics:")273    metrics = results["translation_metrics"]274    print(f"  Success Rate: {metrics['success_rate']:.3f}")275    print(f"  Average Quality: {metrics['average_quality']:.3f}")276 277    print("\nQuality Distribution:")278    dist = metrics["quality_distribution"]279    print(f"  Excellent (>0.8): {dist['excellent']}")280    print(f"  Good (0.6-0.8): {dist['good']}")281    print(f"  Fair (0.4-0.6): {dist['fair']}")282    print(f"  Poor (≤0.4): {dist['poor']}")283 284    print("\nLanguage Coverage:")285    for lang, count in results["language_coverage"].items():286        print(f"  {lang}: {count}")287 288    print("\nKey Insights:")289    for insight in results["insights"]:290        print(f"  • {insight}")291 292    print("\nRecommendations:")293    for rec in results["recommendations"]:294        print(f"  • {rec}")295 296def main():297    """Run cross-language translation benchmark on CodeReality-1T dataset."""298    # Configuration299    data_dir = "/mnt/z/CodeReality_Final/unified_dataset"300    sample_size = 100  # Reduced for planning phase301 302    print("CodeReality-1T Cross-Language Translation Benchmark")303    print("Status: PLANNED - Framework scaffold only")304    print(f"Data directory: {data_dir}")305    print(f"Sample size: {sample_size}")306 307    # Load dataset sample308    print("\nLoading dataset sample...")309    repositories = load_dataset_sample(data_dir, sample_size)310 311    if not repositories:312        print("No repositories loaded - using mock data for demonstration")313        # Create mock data for demonstration314        repositories = [{"name": f"multilang_repo_{i}", "languages": ["python", "javascript"]} for i in range(10)]315 316    # Run benchmark317    results = run_benchmark(repositories)318 319    # Print results320    print_benchmark_results(results)321 322    # Save results323    output_file = "cross_language_translation_results.json"324    with open(output_file, 'w') as f:325        json.dump(results, f, indent=2)326 327    print(f"\nResults saved to: {output_file}")328    print("Note: This is a framework scaffold - full implementation needed")329 330if __name__ == "__main__":331    main()