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"""
Compute the success rate of each model for the SocialMediaManager-A2A task.
Success criteria: the `status` field in `metadata.json` equals "success".
(status="success" means a Shakespeare-style X post was generated and validated.)
"""
import json
import re
from pathlib import Path
from collections import defaultdict
# 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 = "SocialMediaManager-A2A"
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`
Returns:
(success: bool, metadata: dict) - Success flag and the loaded 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 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"⚠️ 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 evaluation
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} times, crew RETRY {crew_retry_count} times)"
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 the summary statistics."""
print("\n" + "=" * 100)
print(f"Model execution summary - {PROJECT_NAME}")
print("Success criteria: status field in metadata.json equals 'success'")
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 for failure samples."""
print("\n" + "=" * 100)
print("Failure sample 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} - Failure samples: {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 the 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 criteria: status field in metadata.json equals 'success'\n")
results = analyze_model_results()
print_summary(results)
print_failure_details(results)
save_detailed_results(results)
save_csv_summary(results)
print("\n Done!")
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