data/processed/RQ1/EmailResponder/README.md

EmailResponder Evaluation Scripts

This directory contains evaluation scripts for analyzing the EmailResponder project results across different LLM models.

Scripts

1. analyze_retry_patterns.py

Analyzes RETRY patterns in EmailResponder project execution paths.

Features:

• Extracts error information from execution paths
• Counts RETRY attempts and locations
• Calculates error rates and RETRY rates per model
• Generates detailed statistics by agent and node type

Output:

• retry_analysis.json - Detailed JSON results
• retry_summary.csv - Summary statistics
• error_by_agent.csv - Error statistics by agent

Usage:

python3 analyze_retry_patterns.py

2. evaluate_success.py

Calculates success rates for each model in EmailResponder tasks.

Features:

• Analyzes execution results from test sessions
• Computes success/failure rates per model
• Tracks individual session outcomes

Output:

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

Usage:

python3 evaluate_success.py

3. evaluate_trajectory.py

Evaluates trajectory metrics for EmailResponder project.

Features:

• Parses execution paths and extracts trajectories
• Evaluates 6 trajectory metrics:
  • Exact match
  • In-order match
  • Any-order match
  • Precision
  • Recall
  • Single-tool use
• Calculates path diversity and entropy

Output:

• CSV file with evaluation results

Usage:

python3 evaluate_trajectory.py --config reference_trajectory.yaml --output evaluation_results.csv

Arguments:

• --config - Reference trajectory config file (YAML format)
• --base-dir - RESULTS directory path (defaults to two levels up)
• --output - Output CSV file path
• --format - Output format (csv only)

Configuration

reference_trajectory.yaml

Defines the reference trajectory (ground truth) for evaluation.

Key sections:

• project_name - Project identifier
• extract_types - Node types to extract (SPAN, Chain, AGENT, LLM, Tool)
• reference_trajectory - Expected execution sequence
• target_tools - Tools to track for single-tool use metric
• models - List of 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 automatically process test results from the configured base directory
• Results are saved in the same directory as the scripts
• Execution paths are parsed from execution_path.md files in test session directories