data/processed/RQ1/SocialMediaManager-MCP/evaluate_success.py

#!/usr/bin/env python3
"""
Compute the success rate of each model on the SocialMediaManager-MCP task.

Success criterion: the `status` field in `metadata.json` equals "success".
(status="success" means a Shakespeare-style X post was generated and passed validation.)
"""

import json
import re
from pathlib import Path
from collections import defaultdict

# Base directory and model list
BASE_DIR = Path("/Users/wzr/TOSEM-2025/RESULTS")
MODELS = [
    "DeepSeek-R1",
    "DeepSeek-V3-1",
    "GPT-4o-mini",
    "GPT-5",
    "Gemini-2.5-flash",
    "Gemini-2.5-flash-nothinking",
    "Qwen3-235b",
]
PROJECT_NAME = "SocialMediaManager-MCP"


def check_status_success(metadata_file: Path) -> tuple[bool, dict]:
    """
    Check whether the `status` field in metadata.json equals "success".

    Args:
        metadata_file: Path to metadata.json file

    Returns:
        (success: bool, metadata: dict) - success flag and metadata
    """
    if not metadata_file.exists():
        return False, {}

    try:
        with open(metadata_file, "r", encoding="utf-8") as f:
            metadata = json.load(f)

        status = metadata.get("status", "unknown")
        return status == "success", metadata

    except Exception as e:
        print(f"  ❌ Error reading {metadata_file}: {e}")
        return False, {}


def analyze_model_results():
    """Analyze execution results for all models."""
    results = {}

    for model in MODELS:
        test_results_dir = BASE_DIR / model / PROJECT_NAME / "test_results"

        if not test_results_dir.exists():
            print(
                f"⚠️  test_results directory not found for model {model}: {test_results_dir}"
            )
            continue

        model_stats = {
            "success_count": 0,
            "failure_count": 0,
            "total_count": 0,
            "sessions": [],
        }

        # Iterate over all session subfolders
        for session_dir in sorted(test_results_dir.iterdir()):
            if not session_dir.is_dir():
                continue

            metadata_file = session_dir / "metadata.json"

            # metadata.json is required for classification
            if not metadata_file.exists():
                continue

            try:
                # Check whether status is success
                is_success, metadata = check_status_success(metadata_file)

                # Extract metadata fields
                topic = metadata.get("topic", "unknown")
                retry_count = metadata.get("retry_count", 0)
                valid = metadata.get("valid", False)
                duration = metadata.get("duration_seconds", 0)
                crew_retry_count = metadata.get("crew_retry_count", 0)
                status = metadata.get("status", "unknown")

                model_stats["total_count"] += 1
                if is_success:
                    model_stats["success_count"] += 1
                else:
                    model_stats["failure_count"] += 1

                # Build status description
                if is_success:
                    reason = f"✅ Success (RETRY {retry_count}, crew RETRY {crew_retry_count})"
                else:
                    reason = f"❌ Failure: status={status}"

                model_stats["sessions"].append(
                    {
                        "session": session_dir.name,
                        "success": is_success,
                        "status": status,
                        "topic": (
                            topic[:80] if isinstance(topic, str) else str(topic)[:80]
                        ),
                        "retry_count": retry_count,
                        "crew_retry_count": crew_retry_count,
                        "valid": valid,
                        "duration_seconds": round(duration, 2),
                        "reason": reason,
                    }
                )

            except Exception as e:
                print(f"❌ Error processing {session_dir}: {e}")

        results[model] = model_stats

    return results


def print_summary(results):
    """Print a summary table."""
    print("\n" + "=" * 100)
    print(f"Model execution summary - {PROJECT_NAME}")
    print("Success criterion: status == 'success' in metadata.json")
    print("=" * 100 + "\n")

    # Header
    print(
        f"{'Model':<35} {'Total':<10} {'Success':<10} {'Failure':<10} {'Success Rate':<15}"
    )
    print("-" * 100)

    for model, stats in results.items():
        total = stats["total_count"]
        success = stats["success_count"]
        failure = stats["failure_count"]

        if total > 0:
            success_rate = (success / total) * 100
            print(
                f"{model:<35} {total:<10} {success:<10} {failure:<10} {success_rate:>6.1f}%"
            )
        else:
            print(f"{model:<35} {'No data':<10}")

    print("=" * 100)


def print_failure_details(results):
    """Print details of failed sessions."""
    print("\n" + "=" * 100)
    print("Failure details")
    print("=" * 100 + "\n")

    for model, stats in results.items():
        failures = [s for s in stats["sessions"] if not s["success"]]
        if not failures:
            continue

        print(f"\n{model} - Failed sessions: {len(failures)}")
        print("-" * 100)

        for i, session in enumerate(failures, 1):
            print(f"{i}. {session['session']}")
            print(f"   Topic: {session['topic']}")
            print(f"   Status: {session['status']}")
            print(f"   Reason: {session['reason']}")
            print()


def save_detailed_results(results, output_file="success_detailed_results.json"):
    """Save detailed results to a JSON file."""
    output_path = Path(__file__).parent / output_file
    with open(output_path, "w", encoding="utf-8") as f:
        json.dump(results, f, indent=2, ensure_ascii=False)
    print(f"\n✅ Detailed results saved to: {output_path}")


def save_csv_summary(results, output_file="success_rate.csv"):
    """Save summary to a CSV file."""
    import csv

    output_path = Path(__file__).parent / output_file
    with open(output_path, "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(
            ["Model", "Total", "Success", "Failure", "Success_Rate(%)", "Criteria"]
        )

        for model, stats in results.items():
            total = stats["total_count"]
            success = stats["success_count"]
            failure = stats["failure_count"]
            success_rate = (success / total * 100) if total > 0 else 0

            writer.writerow(
                [
                    model,
                    total,
                    success,
                    failure,
                    f"{success_rate:.2f}",
                    "status='success' in metadata.json",
                ]
            )

    print(f"✅ CSV summary saved to: {output_path}")


if __name__ == "__main__":
    print(f"Starting success-rate analysis for {PROJECT_NAME}...")
    print("Success criterion: status == 'success' in metadata.json\n")

    results = analyze_model_results()
    print_summary(results)
    print_failure_details(results)
    save_detailed_results(results)
    save_csv_summary(results)

    print("\n✅ Done")