data/processed/RQ1/SQLAssistant-H_A2A/analyze_retry_patterns.py

#!/usr/bin/env python3
"""
Analyze RETRY patterns in SQLAssistant-H_A2A project
Statistics on error locations, RETRY counts, RETRY rates, etc.
Special focus on:
- BUSINESS-RETRY: SPAN names containing (business_retry N) and/or [BUSINESS-RETRY]
- orchestrator / crew_execution RETRY: e.g., [SPAN] crew_execution (retry N) [RETRYN]
"""

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

# 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 = "SQLAssistant-H_A2A"


def extract_error_info(line: str) -> dict:
    """Extract error information from error line

    Returns:
        {'has_error': bool, 'node_type': str, 'node_name': str, 'error_msg': str}
    """
    # Remove tree structure characters (including ─)
    clean = re.sub(r"^[│├└─\-\s]+", "", line).strip()

    # Check for error marker
    if "❌" not in clean:
        return {"has_error": False}

    # Remove error marker
    clean = clean.split("❌", 1)[1].lstrip()

    # Extract node type and name
    node_match = re.match(
        r"\[(SPAN|Chain|AGENT|Tool|LLM)\]\s+([^\[\]]+?)(?:\s+\[ERROR:(.*))?$",
        clean,
    )
    if not node_match:
        return {
            "has_error": True,
            "node_type": "Unknown",
            "node_name": "Unknown",
            "error_msg": "",
        }

    node_type = node_match.group(1)
    node_name = node_match.group(2).strip()
    error_msg = node_match.group(3).strip() if node_match.group(3) else ""

    # Clean node name
    if node_type == "AGENT":
        node_name = re.sub(r"\._execute_core$", "", node_name)
    elif node_type == "Tool":
        node_name = re.sub(r"\._use$", "", node_name)
    elif node_type == "Chain":
        node_name = re.sub(r"Crew_[a-f0-9\-]+\.kickoff", "Crew***.kickoff", node_name)

    return {
        "has_error": True,
        "node_type": node_type,
        "node_name": node_name,
        "error_msg": error_msg,
    }


def extract_retry_info(line: str) -> dict:
    """Extract RETRY information from RETRY line (including BUSINESS-RETRY and orchestrator RETRY)

    In SQLAssistant-H_A2A:
    - BUSINESS-RETRY: e.g.,
      [SPAN] generate_sql (business_retry 1) ... [BUSINESS-RETRY]
    - orchestrator / crew_execution RETRY: e.g.,
      [SPAN] crew_execution (retry 1) [RETRY1] ...

    Returns:
        {
            'is_retry': bool,
            'retry_type': str ('orchestrator' or 'business_logic'),
            'retry_number': int,
            'node_type': str,
            'node_name': str,
            'batch_info': str (for grouping BUSINESS-RETRY location info)
        }
    """
    # Remove tree structure characters (including ─)
    clean = re.sub(r"^[│├└─\-\s]+", "", line).strip()

    # BUSINESS-RETRY marker: (business_retry N) or [BUSINESS-RETRY]
    has_business_retry_flag = "[BUSINESS-RETRY]" in clean or "business_retry" in clean
    business_retry_match = re.search(r"\(business_retry\s+(\d+)\)", clean)

    # orchestrator / crew_execution RETRY marker: (retry N) or [RETRYN]
    orchestrator_retry_match = re.search(r"\(retry\s+(\d+)\)", clean)
    if not orchestrator_retry_match:
        orchestrator_retry_match = re.search(r"\[RETRY(\d+)\]", clean)

    # If neither RETRY type exists, return False
    if not has_business_retry_flag and not orchestrator_retry_match:
        return {"is_retry": False}

    # Determine RETRY type and number
    if orchestrator_retry_match:
        retry_type = "orchestrator"
        retry_number = int(orchestrator_retry_match.group(1))
    else:
        retry_type = "business_logic"
        retry_number = int(business_retry_match.group(1)) if business_retry_match else 0

    # Extract node type and name (may have ❌ prefix)
    node_match = re.match(
        r"(?:❌\s*)?\[(SPAN|Chain|AGENT)\]\s+([^\[\]]+?)(?:\s+\[.*)?$",
        clean,
    )
    if not node_match:
        return {
            "is_retry": True,
            "retry_type": retry_type,
            "retry_number": retry_number,
            "node_type": "Unknown",
            "node_name": "Unknown",
            "batch_info": "",
        }

    node_type = node_match.group(1)
    node_name = node_match.group(2).strip()

    # For BUSINESS-RETRY, use node_name as grouping info (e.g., generate_sql (business_retry 1))
    if retry_type == "business_logic":
        step_name = node_name
        if "business_retry" in step_name:
            step_name = step_name.split(" (business_retry", 1)[0].strip()
        batch_info = step_name
    else:
        batch_info = ""

    return {
        "is_retry": True,
        "retry_type": retry_type,
        "retry_number": retry_number,
        "node_type": node_type,
        "node_name": node_name,
        "batch_info": batch_info,
    }


def analyze_session(md_file: str) -> dict:
    """Analyze a single session's execution_path.md

    Returns:
        {
            'has_error': bool,
            'has_retry': bool,
            'has_orchestrator_retry': bool,
            'has_business_retry': bool,
            'max_retry_number': int,
            'total_orchestrator_retries': int,
            'total_business_retries': int,
            'errors': [{'node_type': str, 'node_name': str, 'error_msg': str}, ...],
            'retries': [{
                'retry_type': str,
                'retry_number': int,
                'node_type': str,
                'node_name': str,
                'batch_info': str
            }, ...]
        }
    """
    if not os.path.exists(md_file):
        return None

    with open(md_file, "r", encoding="utf-8") as f:
        content = f.read()

    # Extract Execution Path Tree section
    tree_match = re.search(
        r"## Execution Path Tree.*?```\n(.*?)```", content, re.DOTALL
    )
    if not tree_match:
        return None

    tree_content = tree_match.group(1)

    errors = []
    retries = []

    for line in tree_content.split("\n"):
        if not line.strip():
            continue

        # Check for errors
        error_info = extract_error_info(line)
        if error_info["has_error"]:
            errors.append(
                {
                    "node_type": error_info.get("node_type", "Unknown"),
                    "node_name": error_info.get("node_name", "Unknown"),
                    "error_msg": error_info.get("error_msg", ""),
                }
            )

        # Check for RETRY (including BUSINESS-RETRY and orchestrator RETRY)
        retry_info = extract_retry_info(line)
        if retry_info["is_retry"]:
            retries.append(
                {
                    "retry_type": retry_info["retry_type"],
                    "retry_number": retry_info["retry_number"],
                    "node_type": retry_info["node_type"],
                    "node_name": retry_info["node_name"],
                    "batch_info": retry_info["batch_info"],
                }
            )

    # Count each type of RETRY
    orchestrator_retries = [r for r in retries if r["retry_type"] == "orchestrator"]
    business_retries = [r for r in retries if r["retry_type"] == "business_logic"]

    max_retry = (
        max([r["retry_number"] for r in orchestrator_retries])
        if orchestrator_retries
        else 0
    )

    return {
        "has_error": len(errors) > 0,
        "has_retry": len(retries) > 0,
        "has_orchestrator_retry": len(orchestrator_retries) > 0,
        "has_business_retry": len(business_retries) > 0,
        "max_retry_number": max_retry,
        "total_orchestrator_retries": len(orchestrator_retries),
        "total_business_retries": len(business_retries),
        "errors": errors,
        "retries": retries,
    }


def collect_model_stats(model_name: str) -> dict:
    """Collect RETRY statistics for a single model

    Returns:
        {
            'model': str,
            'total_sessions': int,
            'sessions_with_error': int,
            'sessions_with_retry': int,
            'sessions_with_orchestrator_retry': int,
            'sessions_with_business_retry': int,
            'total_orchestrator_retry_attempts': int,
            'total_business_retry_attempts': int,
            'error_by_agent': {agent_name: count},
            'error_types': {error_msg: count},
            'max_retry_number': int,
            'business_retry_chapters': {chapter_info: count},
            'session_details': [...]
        }
    """
    test_results_dir = BASE_DIR / model_name / PROJECT_NAME / "test_results"

    if not test_results_dir.exists():
        return None

    stats = {
        "model": model_name,
        "total_sessions": 0,
        "sessions_with_error": 0,
        "sessions_with_retry": 0,
        "sessions_with_orchestrator_retry": 0,
        "sessions_with_business_retry": 0,
        "sessions_with_only_orchestrator_retry": 0,
        "sessions_with_only_business_retry": 0,
        "sessions_with_both_retries": 0,
        "total_orchestrator_retry_attempts": 0,
        "total_business_retry_attempts": 0,
        "error_by_agent": defaultdict(int),
        "error_by_node_type": defaultdict(int),
        "error_types": defaultdict(int),
        "max_retry_number": 0,
        "business_retry_chapters": defaultdict(int),
        "session_details": [],
    }

    for session_dir in sorted(test_results_dir.iterdir()):
        if not session_dir.is_dir():
            continue

        exec_path_file = session_dir / "execution_path.md"
        if not exec_path_file.exists():
            continue

        stats["total_sessions"] += 1

        analysis = analyze_session(str(exec_path_file))
        if not analysis:
            continue

        # Count errors and RETRYs
        if analysis["has_error"]:
            stats["sessions_with_error"] += 1

        if analysis["has_retry"]:
            stats["sessions_with_retry"] += 1

        if analysis["has_orchestrator_retry"]:
            stats["sessions_with_orchestrator_retry"] += 1
            stats["total_orchestrator_retry_attempts"] += analysis[
                "total_orchestrator_retries"
            ]
            stats["max_retry_number"] = max(
                stats["max_retry_number"], analysis["max_retry_number"]
            )

        if analysis["has_business_retry"]:
            stats["sessions_with_business_retry"] += 1
            stats["total_business_retry_attempts"] += analysis["total_business_retries"]

        # Count sessions with only one type of RETRY and sessions with both types
        if analysis["has_orchestrator_retry"] and not analysis["has_business_retry"]:
            stats["sessions_with_only_orchestrator_retry"] += 1
        elif analysis["has_business_retry"] and not analysis["has_orchestrator_retry"]:
            stats["sessions_with_only_business_retry"] += 1
        elif analysis["has_orchestrator_retry"] and analysis["has_business_retry"]:
            stats["sessions_with_both_retries"] += 1

        # Count error locations
        for error in analysis["errors"]:
            if error["node_type"] == "AGENT":
                stats["error_by_agent"][error["node_name"]] += 1
            stats["error_by_node_type"][error["node_type"]] += 1

            # Extract short description of error type
            error_msg = error["error_msg"]
            if error_msg:
                # Take first 100 characters as error type identifier
                error_type = (
                    error_msg[:100]
                    if len(error_msg) <= 100
                    else error_msg[:100] + "..."
                )
                stats["error_types"][error_type] += 1

        # Count BUSINESS-RETRY locations (reuse business_retry_chapters field name)
        for retry in analysis["retries"]:
            if retry["retry_type"] == "business_logic" and retry["batch_info"]:
                stats["business_retry_chapters"][retry["batch_info"]] += 1

        # Save session details
        stats["session_details"].append(
            {
                "session": session_dir.name,
                "has_error": analysis["has_error"],
                "has_retry": analysis["has_retry"],
                "has_orchestrator_retry": analysis["has_orchestrator_retry"],
                "has_business_retry": analysis["has_business_retry"],
                "orchestrator_retry_count": analysis["total_orchestrator_retries"],
                "business_retry_count": analysis["total_business_retries"],
                "errors": analysis["errors"],
                "retries": analysis["retries"],
            }
        )

    # Calculate RETRY rates
    total = stats["total_sessions"]
    stats["retry_rate"] = (
        (stats["sessions_with_retry"] / total * 100) if total > 0 else 0
    )
    stats["orchestrator_retry_rate"] = (
        (stats["sessions_with_orchestrator_retry"] / total * 100) if total > 0 else 0
    )
    stats["business_retry_rate"] = (
        (stats["sessions_with_business_retry"] / total * 100) if total > 0 else 0
    )
    stats["error_rate"] = (
        (stats["sessions_with_error"] / total * 100) if total > 0 else 0
    )

    # Convert defaultdict to regular dict
    stats["error_by_agent"] = dict(stats["error_by_agent"])
    stats["error_by_node_type"] = dict(stats["error_by_node_type"])
    stats["error_types"] = dict(stats["error_types"])
    stats["business_retry_chapters"] = dict(stats["business_retry_chapters"])

    return stats


def print_summary(all_stats):
    """Print statistics summary"""
    print("\n" + "=" * 120)
    print(f"RETRY Pattern Analysis Summary - {PROJECT_NAME}")
    print("=" * 120 + "\n")

    # Overall statistics table
    print("## Statistics by Model\n")
    header = f"{'Model':<30} {'Total Sessions':<10} {'Error Rate':<10} {'Total RETRY Rate':<10} {'Orch RETRY Rate':<12} {'Business RETRY Rate':<12} {'Orch RETRY Count':<12} {'Business RETRY Count':<12}"
    print(header)
    print("-" * 120)

    for stats in all_stats:
        if stats:
            print(
                f"{stats['model']:<30} {stats['total_sessions']:<10} "
                f"{stats['error_rate']:>8.1f}% {stats['retry_rate']:>8.1f}% "
                f"{stats['orchestrator_retry_rate']:>10.1f}% {stats['business_retry_rate']:>10.1f}% "
                f"{stats['total_orchestrator_retry_attempts']:<12} {stats['total_business_retry_attempts']:<12}"
            )

    print("\n" + "=" * 120)

    # Detailed information for each model
    for stats in all_stats:
        if not stats:
            continue

        print(f"\n### 📊 {stats['model']}\n")
        print(f"- **Total Sessions**: {stats['total_sessions']}")
        print(
            f"- **Sessions with Errors**: {stats['sessions_with_error']} ({stats['error_rate']:.1f}%)"
        )
        print(
            f"- **Sessions with RETRY**: {stats['sessions_with_retry']} ({stats['retry_rate']:.1f}%)"
        )
        print(
            f"  - Orchestrator RETRY: {stats['sessions_with_orchestrator_retry']} ({stats['orchestrator_retry_rate']:.1f}%) "
            f"[Only Orch: {stats['sessions_with_only_orchestrator_retry']}]"
        )
        print(
            f"  - BUSINESS-RETRY: {stats['sessions_with_business_retry']} ({stats['business_retry_rate']:.1f}%) "
            f"[Only Business: {stats['sessions_with_only_business_retry']}]"
        )
        print(f"  - Both: {stats['sessions_with_both_retries']}")
        print(f"- **Total RETRY Count**:")
        print(
            f"  - Orchestrator RETRY: {stats['total_orchestrator_retry_attempts']} times"
        )
        print(f"  - BUSINESS-RETRY: {stats['total_business_retry_attempts']} times")
        print(f"- **Max Orchestrator RETRY Number**: {stats['max_retry_number']}")

        if stats["error_by_agent"]:
            print(f"\n**Errors by AGENT**:")
            for agent, count in sorted(
                stats["error_by_agent"].items(), key=lambda x: x[1], reverse=True
            )[:5]:
                print(f"  - {agent}: {count} times")

        if stats["business_retry_chapters"]:
            print(f"\n**BUSINESS-RETRY Locations (Top 10)**:")
            for chapter, count in sorted(
                stats["business_retry_chapters"].items(),
                key=lambda x: x[1],
                reverse=True,
            )[:10]:
                print(f"  - [{count} times] {chapter}")

        if stats["error_types"]:
            print(f"\n**Error Types (Top 3)**:")
            for error_type, count in sorted(
                stats["error_types"].items(), key=lambda x: x[1], reverse=True
            )[:3]:
                print(f"  - [{count} times] {error_type}")

        print("\n" + "-" * 120)


def save_results(all_stats):
    """Save results to files"""
    output_dir = Path(__file__).parent

    # Save detailed JSON
    json_file = output_dir / "retry_analysis.json"
    json_data = []
    for stats in all_stats:
        if stats:
            json_data.append(stats)

    with open(json_file, "w", encoding="utf-8") as f:
        json.dump(json_data, f, indent=2, ensure_ascii=False)
    print(f"\n✅ Detailed JSON results saved: {json_file}")

    # Save CSV summary
    csv_file = output_dir / "retry_summary.csv"
    with open(csv_file, "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(
            [
                "Model",
                "Total_Sessions",
                "Sessions_With_Error",
                "Error_Rate(%)",
                "Sessions_With_Retry",
                "Retry_Rate(%)",
                "Orchestrator_Retry_Sessions",
                "Orchestrator_Retry_Rate(%)",
                "Only_Orchestrator_Retry_Sessions",
                "Business_Retry_Sessions",
                "Business_Retry_Rate(%)",
                "Only_Business_Retry_Sessions",
                "Both_Retries_Sessions",
                "Total_Orchestrator_Retries",
                "Total_Business_Retries",
                "Max_Retry_Number",
                "Generate_SQL_Retries",
                "Check_Compliance_Retries",
            ]
        )

        for stats in all_stats:
            if stats:
                writer.writerow(
                    [
                        stats["model"],
                        stats["total_sessions"],
                        stats["sessions_with_error"],
                        f"{stats['error_rate']:.2f}",
                        stats["sessions_with_retry"],
                        f"{stats['retry_rate']:.2f}",
                        stats["sessions_with_orchestrator_retry"],
                        f"{stats['orchestrator_retry_rate']:.2f}",
                        stats["sessions_with_only_orchestrator_retry"],
                        stats["sessions_with_business_retry"],
                        f"{stats['business_retry_rate']:.2f}",
                        stats["sessions_with_only_business_retry"],
                        stats["sessions_with_both_retries"],
                        stats["total_orchestrator_retry_attempts"],
                        stats["total_business_retry_attempts"],
                        stats["max_retry_number"],
                        stats["business_retry_chapters"].get("generate_sql", 0),
                        stats["business_retry_chapters"].get("check_compliance", 0),
                    ]
                )

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

    # Save error location statistics (by AGENT)
    error_csv_file = output_dir / "error_by_agent.csv"
    with open(error_csv_file, "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(["Model", "Agent_Name", "Error_Count"])

        for stats in all_stats:
            if stats and stats["error_by_agent"]:
                for agent, count in sorted(
                    stats["error_by_agent"].items(), key=lambda x: x[1], reverse=True
                ):
                    writer.writerow([stats["model"], agent, count])

    print(f"✅ Error location statistics (by AGENT) saved: {error_csv_file}")

    # Save BUSINESS-RETRY location statistics (aggregated by business Span)
    chapter_csv_file = output_dir / "business_retry_chapters.csv"
    with open(chapter_csv_file, "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(["Model", "Business_Span", "Retry_Count"])

        for stats in all_stats:
            if stats and stats["business_retry_chapters"]:
                for chapter, count in sorted(
                    stats["business_retry_chapters"].items(),
                    key=lambda x: x[1],
                    reverse=True,
                ):
                    writer.writerow([stats["model"], chapter, count])

    print(f"✅ BUSINESS-RETRY location statistics saved: {chapter_csv_file}")


if __name__ == "__main__":
    print(f"Starting RETRY pattern analysis - {PROJECT_NAME}...")
    print("RETRY types:")
    print("  1. Orchestrator RETRY: e.g., [SPAN] crew_execution (retry N) [RETRYN]")
    print(
        "  2. BUSINESS-RETRY: e.g., [SPAN] generate_sql (business_retry N) ... [BUSINESS-RETRY]\n"
    )

    all_stats = []
    for model in MODELS:
        print(f"\n📊 Analyzing model: {model}")
        stats = collect_model_stats(model)
        if stats:
            all_stats.append(stats)
            print(
                f"  ✅ Completed: {stats['total_sessions']} sessions, "
                f"{stats['sessions_with_error']} errors, "
                f"{stats['sessions_with_orchestrator_retry']} orch-retries, "
                f"{stats['sessions_with_business_retry']} business-retries"
            )
        else:
            print(f"  ⚠️  Skipped (directory does not exist)")

    print_summary(all_stats)
    save_results(all_stats)

    print("\n" + "=" * 120)
    print("✅ Analysis completed!")
    print("=" * 120)