data/processed/RQ1/MarkdownValidator-MCP/README.md

MarkdownValidator-MCP Evaluation Scripts

This directory contains evaluation scripts for analyzing the MarkdownValidator-MCP project results across different LLM models.

Scripts

1. analyze_retry_patterns.py

Analyzes RETRY patterns in execution paths, including:

• Error locations and frequencies
• RETRY counts and rates
• Error distribution by agent and node type

Usage:

python3 analyze_retry_patterns.py

Outputs:

• retry_analysis.json - Detailed retry statistics
• retry_summary.csv - Summary table
• error_by_agent.csv - Error breakdown by agent

2. evaluate_scores.py

Collects and analyzes score statistics for each model.

Usage:

python3 evaluate_scores.py

Outputs:

• score_analysis.json - Detailed score data
• score_summary.csv - Summary statistics (mean, min, max, perfect rate)

3. evaluate_success.py

Calculates success/failure rates for each model.

Usage:

python3 evaluate_success.py

Outputs:

• success-finish_detailed_results.json - Detailed execution results
• success-finish_rate.csv - Success rate summary

4. evaluate_trajectory.py

Evaluates trajectory metrics including:

• Exact match
• In-order match
• Any-order match
• Precision and Recall
• Single-tool use
• Path diversity (unique_path_ratio)
• Path entropy

Usage:

python3 evaluate_trajectory.py --config reference_trajectory.yaml

Options:

• --config - Path to reference trajectory YAML file (default: reference_trajectory.yaml)
• --base-dir - RESULTS directory path (default: two levels up from script)
• --output - Output CSV file path (default: evaluation_results.csv)
• --format - Output format: csv or both (default: csv)

Outputs:

• evaluation_results.csv - Trajectory evaluation results

Configuration

reference_trajectory.yaml

Defines the ideal reference trajectory for evaluation, including:

• Project name and extraction types
• Reference trajectory steps
• Target tools for evaluation
• Models to evaluate

Models Evaluated

• GPT-5
• GPT-4o-mini
• DeepSeek-V3-1
• DeepSeek-R1
• Gemini-2.5-flash
• Gemini-2.5-flash-nothinking
• Qwen3-235b

Requirements

• Python 3.7+
• pandas
• pyyaml

Notes

• All scripts read from /Users/wzr/TOSEM-2025/RESULTS by default
• Scripts output CSV and JSON files for further analysis
• Trajectory evaluation supports wildcard matching for flexible comparison