749 lines
20 KiB
Markdown
749 lines
20 KiB
Markdown
# Multi-Agent System Evaluation Methodology
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## Overview
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This document provides a comprehensive methodology for evaluating multi-agent systems across multiple dimensions including performance, reliability, cost-effectiveness, and user satisfaction. The methodology is designed to provide actionable insights for system optimization.
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## Evaluation Framework
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### Evaluation Dimensions
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#### 1. Task Performance
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- **Success Rate:** Percentage of tasks completed successfully
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- **Completion Time:** Time from task initiation to completion
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- **Quality Metrics:** Accuracy, relevance, completeness of results
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- **Partial Success:** Progress made on incomplete tasks
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#### 2. System Reliability
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- **Availability:** System uptime and accessibility
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- **Error Rates:** Frequency and types of errors
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- **Recovery Time:** Time to recover from failures
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- **Fault Tolerance:** System behavior under component failures
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#### 3. Cost Efficiency
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- **Resource Utilization:** CPU, memory, network, storage usage
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- **Token Consumption:** LLM API usage and costs
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- **Operational Costs:** Infrastructure and maintenance costs
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- **Cost per Task:** Economic efficiency per completed task
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#### 4. User Experience
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- **Response Time:** User-perceived latency
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- **User Satisfaction:** Qualitative feedback scores
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- **Usability:** Ease of system interaction
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- **Predictability:** Consistency of system behavior
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#### 5. Scalability
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- **Load Handling:** Performance under increasing load
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- **Resource Scaling:** Ability to scale resources dynamically
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- **Concurrency:** Handling multiple simultaneous requests
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- **Degradation Patterns:** Behavior at capacity limits
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#### 6. Security
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- **Access Control:** Authentication and authorization effectiveness
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- **Data Protection:** Privacy and confidentiality measures
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- **Audit Trail:** Logging and monitoring completeness
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- **Vulnerability Assessment:** Security weakness identification
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## Metrics Collection
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### Core Metrics
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#### Performance Metrics
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```json
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{
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"task_metrics": {
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"task_id": "string",
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"agent_id": "string",
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"task_type": "string",
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"start_time": "ISO 8601 timestamp",
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"end_time": "ISO 8601 timestamp",
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"duration_ms": "integer",
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"status": "success|failure|partial|timeout",
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"quality_score": "float 0-1",
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"steps_completed": "integer",
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"total_steps": "integer"
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}
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}
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```
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#### Resource Metrics
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```json
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{
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"resource_metrics": {
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"timestamp": "ISO 8601 timestamp",
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"agent_id": "string",
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"cpu_usage_percent": "float",
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"memory_usage_mb": "integer",
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"network_bytes_sent": "integer",
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"network_bytes_received": "integer",
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"tokens_consumed": "integer",
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"api_calls_made": "integer"
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}
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}
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```
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#### Error Metrics
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```json
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{
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"error_metrics": {
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"timestamp": "ISO 8601 timestamp",
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"error_type": "string",
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"error_code": "string",
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"error_message": "string",
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"agent_id": "string",
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"task_id": "string",
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"severity": "critical|high|medium|low",
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"recovery_action": "string",
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"resolved": "boolean"
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}
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}
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```
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### Advanced Metrics
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#### Agent Collaboration Metrics
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```json
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{
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"collaboration_metrics": {
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"timestamp": "ISO 8601 timestamp",
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"initiating_agent": "string",
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"target_agent": "string",
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"interaction_type": "request|response|broadcast|delegate",
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"latency_ms": "integer",
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"success": "boolean",
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"payload_size_bytes": "integer",
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"context_shared": "boolean"
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}
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}
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```
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#### Tool Usage Metrics
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```json
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{
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"tool_metrics": {
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"timestamp": "ISO 8601 timestamp",
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"agent_id": "string",
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"tool_name": "string",
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"invocation_duration_ms": "integer",
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"success": "boolean",
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"error_type": "string|null",
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"input_size_bytes": "integer",
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"output_size_bytes": "integer",
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"cached_result": "boolean"
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}
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}
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```
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## Evaluation Methods
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### 1. Synthetic Benchmarks
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#### Task Complexity Levels
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- **Level 1 (Simple):** Single-agent, single-tool tasks
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- **Level 2 (Moderate):** Multi-tool tasks requiring coordination
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- **Level 3 (Complex):** Multi-agent collaborative tasks
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- **Level 4 (Advanced):** Long-running, multi-stage workflows
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- **Level 5 (Expert):** Adaptive tasks requiring learning
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#### Benchmark Task Categories
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```yaml
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benchmark_categories:
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information_retrieval:
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- simple_web_search
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- multi_source_research
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- fact_verification
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- comparative_analysis
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content_generation:
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- text_summarization
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- creative_writing
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- technical_documentation
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- multilingual_translation
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data_processing:
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- data_cleaning
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- statistical_analysis
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- visualization_creation
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- report_generation
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problem_solving:
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- algorithm_development
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- optimization_tasks
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- troubleshooting
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- decision_support
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workflow_automation:
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- multi_step_processes
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- conditional_workflows
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- exception_handling
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- resource_coordination
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```
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#### Benchmark Execution
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```python
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def run_benchmark_suite(agents, benchmark_tasks):
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results = {}
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for category, tasks in benchmark_tasks.items():
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category_results = []
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for task in tasks:
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task_result = execute_benchmark_task(
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agents=agents,
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task=task,
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timeout=task.max_duration,
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repetitions=task.repetitions
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)
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category_results.append(task_result)
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results[category] = analyze_category_results(category_results)
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return generate_benchmark_report(results)
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```
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### 2. A/B Testing
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#### Test Design
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```yaml
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ab_test_design:
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hypothesis: "New agent architecture improves task success rate"
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success_metrics:
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primary: "task_success_rate"
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secondary: ["response_time", "cost_per_task", "user_satisfaction"]
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test_configuration:
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control_group: "current_architecture"
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treatment_group: "new_architecture"
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traffic_split: 50/50
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duration_days: 14
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minimum_sample_size: 1000
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statistical_parameters:
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confidence_level: 0.95
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minimum_detectable_effect: 0.05
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statistical_power: 0.8
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```
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#### Analysis Framework
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```python
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def analyze_ab_test(control_data, treatment_data, metrics):
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results = {}
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for metric in metrics:
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control_values = extract_metric_values(control_data, metric)
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treatment_values = extract_metric_values(treatment_data, metric)
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# Statistical significance test
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stat_result = perform_statistical_test(
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control_values,
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treatment_values,
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test_type=determine_test_type(metric)
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)
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# Effect size calculation
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effect_size = calculate_effect_size(
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control_values,
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treatment_values
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)
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results[metric] = {
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"control_mean": np.mean(control_values),
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"treatment_mean": np.mean(treatment_values),
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"p_value": stat_result.p_value,
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"confidence_interval": stat_result.confidence_interval,
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"effect_size": effect_size,
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"practical_significance": assess_practical_significance(
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effect_size, metric
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)
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}
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return results
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```
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### 3. Load Testing
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#### Load Test Scenarios
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```yaml
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load_test_scenarios:
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baseline_load:
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concurrent_users: 10
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ramp_up_time: "5 minutes"
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duration: "30 minutes"
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normal_load:
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concurrent_users: 100
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ramp_up_time: "10 minutes"
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duration: "1 hour"
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peak_load:
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concurrent_users: 500
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ramp_up_time: "15 minutes"
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duration: "2 hours"
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stress_test:
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concurrent_users: 1000
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ramp_up_time: "20 minutes"
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duration: "1 hour"
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spike_test:
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phases:
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- users: 100, duration: "10 minutes"
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- users: 1000, duration: "5 minutes" # Spike
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- users: 100, duration: "15 minutes"
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```
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#### Performance Thresholds
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```yaml
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performance_thresholds:
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response_time:
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p50: 2000ms # 50th percentile
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p90: 5000ms # 90th percentile
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p95: 8000ms # 95th percentile
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p99: 15000ms # 99th percentile
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throughput:
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minimum: 10 # requests per second
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target: 50 # requests per second
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error_rate:
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maximum: 5% # percentage of failed requests
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resource_utilization:
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cpu_max: 80%
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memory_max: 85%
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network_max: 70%
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```
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### 4. Real-World Evaluation
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#### Production Monitoring
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```yaml
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production_metrics:
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business_metrics:
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- task_completion_rate
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- user_retention_rate
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- feature_adoption_rate
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- time_to_value
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technical_metrics:
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- system_availability
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- mean_time_to_recovery
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- resource_efficiency
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- cost_per_transaction
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user_experience_metrics:
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- net_promoter_score
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- user_satisfaction_rating
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- task_abandonment_rate
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- help_desk_ticket_volume
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```
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#### Continuous Evaluation Pipeline
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```python
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class ContinuousEvaluationPipeline:
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def __init__(self, metrics_collector, analyzer, alerting):
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self.metrics_collector = metrics_collector
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self.analyzer = analyzer
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self.alerting = alerting
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def run_evaluation_cycle(self):
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# Collect recent metrics
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metrics = self.metrics_collector.collect_recent_metrics(
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time_window="1 hour"
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)
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# Analyze performance
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analysis = self.analyzer.analyze_metrics(metrics)
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# Check for anomalies
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anomalies = self.analyzer.detect_anomalies(
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metrics,
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baseline_window="24 hours"
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)
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# Generate alerts if needed
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if anomalies:
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self.alerting.send_alerts(anomalies)
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# Update performance baselines
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self.analyzer.update_baselines(metrics)
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return analysis
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```
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## Analysis Techniques
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### 1. Statistical Analysis
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#### Descriptive Statistics
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```python
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def calculate_descriptive_stats(data):
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return {
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"count": len(data),
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"mean": np.mean(data),
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"median": np.median(data),
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"std_dev": np.std(data),
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"min": np.min(data),
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"max": np.max(data),
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"percentiles": {
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"p25": np.percentile(data, 25),
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"p50": np.percentile(data, 50),
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"p75": np.percentile(data, 75),
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"p90": np.percentile(data, 90),
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"p95": np.percentile(data, 95),
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"p99": np.percentile(data, 99)
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}
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}
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```
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#### Correlation Analysis
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```python
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def analyze_metric_correlations(metrics_df):
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correlation_matrix = metrics_df.corr()
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# Identify strong correlations
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strong_correlations = []
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for i in range(len(correlation_matrix.columns)):
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for j in range(i + 1, len(correlation_matrix.columns)):
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corr_value = correlation_matrix.iloc[i, j]
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if abs(corr_value) > 0.7: # Strong correlation threshold
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strong_correlations.append({
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"metric1": correlation_matrix.columns[i],
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"metric2": correlation_matrix.columns[j],
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"correlation": corr_value,
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"strength": "strong" if abs(corr_value) > 0.8 else "moderate"
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})
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return strong_correlations
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```
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### 2. Trend Analysis
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#### Time Series Analysis
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```python
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def analyze_performance_trends(time_series_data, metric):
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# Decompose time series
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decomposition = seasonal_decompose(
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time_series_data[metric],
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model='additive',
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period=24 # Daily seasonality
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)
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# Trend detection
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trend_slope = calculate_trend_slope(decomposition.trend)
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# Seasonality detection
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seasonal_patterns = identify_seasonal_patterns(decomposition.seasonal)
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# Anomaly detection
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anomalies = detect_anomalies_isolation_forest(time_series_data[metric])
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return {
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"trend_direction": "increasing" if trend_slope > 0 else "decreasing" if trend_slope < 0 else "stable",
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"trend_strength": abs(trend_slope),
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"seasonal_patterns": seasonal_patterns,
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"anomalies": anomalies,
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"forecast": generate_forecast(time_series_data[metric], periods=24)
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}
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```
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### 3. Comparative Analysis
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#### Multi-System Comparison
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```python
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def compare_systems(system_metrics_dict):
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comparison_results = {}
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metrics_to_compare = [
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"success_rate", "average_response_time",
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"cost_per_task", "error_rate"
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]
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for metric in metrics_to_compare:
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metric_values = {
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system: metrics[metric]
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for system, metrics in system_metrics_dict.items()
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}
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# Rank systems by metric
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ranked_systems = sorted(
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metric_values.items(),
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key=lambda x: x[1],
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reverse=(metric in ["success_rate"]) # Higher is better for some metrics
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)
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# Calculate relative performance
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best_value = ranked_systems[0][1]
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relative_performance = {
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system: value / best_value if best_value > 0 else 0
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for system, value in metric_values.items()
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}
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comparison_results[metric] = {
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"rankings": ranked_systems,
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"relative_performance": relative_performance,
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"best_system": ranked_systems[0][0]
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}
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return comparison_results
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```
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## Quality Assurance
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### 1. Data Quality Validation
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#### Data Completeness Checks
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```python
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def validate_data_completeness(metrics_data):
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completeness_report = {}
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required_fields = [
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"timestamp", "task_id", "agent_id",
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"duration_ms", "status", "success"
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]
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for field in required_fields:
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missing_count = metrics_data[field].isnull().sum()
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total_count = len(metrics_data)
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completeness_percentage = (total_count - missing_count) / total_count * 100
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completeness_report[field] = {
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"completeness_percentage": completeness_percentage,
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"missing_count": missing_count,
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"status": "pass" if completeness_percentage >= 95 else "fail"
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}
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return completeness_report
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```
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#### Data Consistency Checks
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```python
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def validate_data_consistency(metrics_data):
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consistency_issues = []
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# Check timestamp ordering
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if not metrics_data['timestamp'].is_monotonic_increasing:
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consistency_issues.append("Timestamps are not in chronological order")
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# Check duration consistency
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duration_negative = (metrics_data['duration_ms'] < 0).sum()
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if duration_negative > 0:
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consistency_issues.append(f"Found {duration_negative} negative durations")
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# Check status-success consistency
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success_status_mismatch = (
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(metrics_data['status'] == 'success') != metrics_data['success']
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).sum()
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if success_status_mismatch > 0:
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consistency_issues.append(f"Found {success_status_mismatch} status-success mismatches")
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return consistency_issues
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```
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### 2. Evaluation Reliability
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#### Reproducibility Framework
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```python
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class ReproducibleEvaluation:
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def __init__(self, config):
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self.config = config
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self.random_seed = config.get('random_seed', 42)
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def setup_environment(self):
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# Set random seeds
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random.seed(self.random_seed)
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np.random.seed(self.random_seed)
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# Configure logging
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self.setup_evaluation_logging()
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# Snapshot system state
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self.snapshot_system_state()
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def run_evaluation(self, test_suite):
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self.setup_environment()
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# Execute evaluation with full logging
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results = self.execute_test_suite(test_suite)
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# Verify reproducibility
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self.verify_reproducibility(results)
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return results
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```
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## Reporting Framework
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### 1. Executive Summary Report
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#### Key Performance Indicators
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```yaml
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kpi_dashboard:
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overall_health_score: 85/100
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performance:
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task_success_rate: 94.2%
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average_response_time: 2.3s
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p95_response_time: 8.1s
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reliability:
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system_uptime: 99.8%
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error_rate: 2.1%
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mean_recovery_time: 45s
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cost_efficiency:
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cost_per_task: $0.05
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token_utilization: 78%
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resource_efficiency: 82%
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user_satisfaction:
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net_promoter_score: 42
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task_completion_rate: 89%
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|
user_retention_rate: 76%
|
|
```
|
|
|
|
#### Trend Indicators
|
|
```yaml
|
|
trend_analysis:
|
|
performance_trends:
|
|
success_rate: "↗ +2.3% vs last month"
|
|
response_time: "↘ -15% vs last month"
|
|
error_rate: "→ stable vs last month"
|
|
|
|
cost_trends:
|
|
total_cost: "↗ +8% vs last month"
|
|
cost_per_task: "↘ -5% vs last month"
|
|
efficiency: "↗ +12% vs last month"
|
|
```
|
|
|
|
### 2. Technical Deep-Dive Report
|
|
|
|
#### Performance Analysis
|
|
```markdown
|
|
## Performance Analysis
|
|
|
|
### Task Success Patterns
|
|
- **Overall Success Rate**: 94.2% (target: 95%)
|
|
- **By Task Type**:
|
|
- Simple tasks: 98.1% success
|
|
- Complex tasks: 87.4% success
|
|
- Multi-agent tasks: 91.2% success
|
|
|
|
### Response Time Distribution
|
|
- **Median**: 1.8 seconds
|
|
- **95th Percentile**: 8.1 seconds
|
|
- **Peak Hours Impact**: +35% slower during 9-11 AM
|
|
|
|
### Error Analysis
|
|
- **Top Error Types**:
|
|
1. Timeout errors (34% of failures)
|
|
2. Rate limit exceeded (28% of failures)
|
|
3. Invalid input (19% of failures)
|
|
```
|
|
|
|
#### Resource Utilization
|
|
```markdown
|
|
## Resource Utilization
|
|
|
|
### Compute Resources
|
|
- **CPU Utilization**: 45% average, 78% peak
|
|
- **Memory Usage**: 6.2GB average, 12.1GB peak
|
|
- **Network I/O**: 125 MB/s average
|
|
|
|
### API Usage
|
|
- **Token Consumption**: 2.4M tokens/day
|
|
- **Cost Breakdown**:
|
|
- GPT-4: 68% of token costs
|
|
- GPT-3.5: 28% of token costs
|
|
- Other models: 4% of token costs
|
|
```
|
|
|
|
### 3. Actionable Recommendations
|
|
|
|
#### Performance Optimization
|
|
```yaml
|
|
recommendations:
|
|
high_priority:
|
|
- title: "Reduce timeout error rate"
|
|
impact: "Could improve success rate by 2.1%"
|
|
effort: "Medium"
|
|
timeline: "2 weeks"
|
|
|
|
- title: "Optimize complex task handling"
|
|
impact: "Could improve complex task success by 5%"
|
|
effort: "High"
|
|
timeline: "4 weeks"
|
|
|
|
medium_priority:
|
|
- title: "Implement intelligent caching"
|
|
impact: "Could reduce costs by 15%"
|
|
effort: "Medium"
|
|
timeline: "3 weeks"
|
|
```
|
|
|
|
## Continuous Improvement Process
|
|
|
|
### 1. Evaluation Cadence
|
|
|
|
#### Regular Evaluation Schedule
|
|
```yaml
|
|
evaluation_schedule:
|
|
real_time:
|
|
frequency: "continuous"
|
|
metrics: ["error_rate", "response_time", "system_health"]
|
|
|
|
hourly:
|
|
frequency: "every hour"
|
|
metrics: ["throughput", "resource_utilization", "user_activity"]
|
|
|
|
daily:
|
|
frequency: "daily at 2 AM UTC"
|
|
metrics: ["success_rates", "cost_analysis", "user_satisfaction"]
|
|
|
|
weekly:
|
|
frequency: "every Sunday"
|
|
metrics: ["trend_analysis", "comparative_analysis", "capacity_planning"]
|
|
|
|
monthly:
|
|
frequency: "first Monday of month"
|
|
metrics: ["comprehensive_evaluation", "benchmark_testing", "strategic_review"]
|
|
```
|
|
|
|
### 2. Performance Baseline Management
|
|
|
|
#### Baseline Update Process
|
|
```python
|
|
def update_performance_baselines(current_metrics, historical_baselines):
|
|
updated_baselines = {}
|
|
|
|
for metric, current_value in current_metrics.items():
|
|
historical_values = historical_baselines.get(metric, [])
|
|
historical_values.append(current_value)
|
|
|
|
# Keep rolling window of last 30 days
|
|
historical_values = historical_values[-30:]
|
|
|
|
# Calculate new baseline
|
|
baseline = {
|
|
"mean": np.mean(historical_values),
|
|
"std": np.std(historical_values),
|
|
"p95": np.percentile(historical_values, 95),
|
|
"trend": calculate_trend(historical_values)
|
|
}
|
|
|
|
updated_baselines[metric] = baseline
|
|
|
|
return updated_baselines
|
|
```
|
|
|
|
## Conclusion
|
|
|
|
Effective evaluation of multi-agent systems requires a comprehensive, multi-dimensional approach that combines quantitative metrics with qualitative assessments. The methodology should be:
|
|
|
|
1. **Comprehensive**: Cover all aspects of system performance
|
|
2. **Continuous**: Provide ongoing monitoring and evaluation
|
|
3. **Actionable**: Generate specific, implementable recommendations
|
|
4. **Adaptable**: Evolve with system changes and requirements
|
|
5. **Reliable**: Produce consistent, reproducible results
|
|
|
|
Regular evaluation using this methodology will ensure multi-agent systems continue to meet user needs while optimizing for cost, performance, and reliability. |