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

#!/usr/bin/env python3
"""
Calculate success rates for models on RecruitmentAssistant-MCP task
Success criteria: reports folder exists and contains multiple (>=2) md files
"""

import json
import os
from pathlib import Path
from collections import defaultdict

# Define base path 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 = "RecruitmentAssistant-MCP"


def check_reports_success(reports_dir: Path) -> dict:
    """
    Check if reports folder meets success criteria

    Success criteria:
    1. reports folder exists
    2. Contains multiple (>=2) md files

    Returns:
        {
            'success': bool,
            'reports_exists': bool,
            'md_count': int,
            'md_files': list
        }
    """
    result = {"success": False, "reports_exists": False, "md_count": 0, "md_files": []}

    # Check if reports folder exists
    if not reports_dir.exists() or not reports_dir.is_dir():
        return result

    result["reports_exists"] = True

    # Count md files
    md_files = sorted([f.name for f in reports_dir.glob("*.md")])
    result["md_count"] = len(md_files)
    result["md_files"] = md_files

    # Determine success: at least 2 md files
    result["success"] = result["md_count"] >= 2

    return result


def analyze_model_results():
    """Analyze execution results for each model"""
    results = {}

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

        if not test_results_dir.exists():
            print(
                f"Warning: 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 through all session subdirectories
        for session_dir in sorted(test_results_dir.iterdir()):
            if not session_dir.is_dir():
                continue

            reports_dir = session_dir / "reports"

            # Check reports folder and md files
            check_result = check_reports_success(reports_dir)

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

            model_stats["sessions"].append(
                {
                    "session": session_dir.name,
                    "success": check_result["success"],
                    "reports_exists": check_result["reports_exists"],
                    "md_count": check_result["md_count"],
                    "md_files": check_result["md_files"],
                }
            )

        results[model] = model_stats

    return results


def print_summary(results):
    """Print statistics summary"""
    print("\n" + "=" * 80)
    print(f"Model Execution Results - {PROJECT_NAME}")
    print("=" * 80 + "\n")

    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}")
            print(f"   Total: {total} runs")
            print(
                f"   Success: {success} runs (reports exists with >=2 md files, {success_rate:.1f}%)"
            )
            print(f"   Failure: {failure} runs ({100-success_rate:.1f}%)")
            print()
        else:
            print(f"{model}")
            print(f"   No data")
            print()

    print("=" * 80)


def save_detailed_results(results, output_file="success_detailed_results.json"):
    """Save detailed results to 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"\nDetailed results saved to: {output_path}")


def save_csv_summary(results, output_file="success_rate.csv"):
    """Save summary to 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(%)"])

        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}"])

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


if __name__ == "__main__":
    print(f"Starting success rate calculation - {PROJECT_NAME}")
    print(
        f"Success criteria: reports folder exists and contains multiple (>=2) md files\n"
    )

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

    print("\nCalculation completed!")