[QA-2] Add Performance Testing Framework

[MVPandey]

⚡ PERFORMANCE TESTING

Priority: HIGH - Performance Validation

Problem

No performance testing framework to validate system capacity, identify bottlenecks, or ensure SLA compliance under load.

Solution

Implement comprehensive performance testing using locust and automated benchmarking.

Load Testing Framework

# tests/performance/locustfile.py
from locust import HttpUser, task, between
import json

class ConversationAnalysisUser(HttpUser):
    wait_time = between(1, 3)
    
    def on_start(self):
        # Setup authentication
        self.headers = {"Authorization": "Bearer test-api-key"}
    
    @task(3)
    def analyze_conversation(self):
        payload = {
            "messages": [
                {"role": "user", "content": "I'm feeling stressed about work"},
                {"role": "assistant", "content": "I understand. Can you tell me more?"}
            ],
            "config": {"mcts_iterations": 2, "num_branches": 2}
        }
        
        with self.client.post(
            "/api/analyze",
            json=payload,
            headers=self.headers,
            catch_response=True
        ) as response:
            if response.status_code == 200:
                result = response.json()
                if "best_response" in result:
                    response.success()
                else:
                    response.failure("Missing best_response")
            else:
                response.failure(f"HTTP {response.status_code}")

    @task(1)
    def health_check(self):
        self.client.get("/health")

# Performance test scenarios
class StressTest(ConversationAnalysisUser):
    # High load scenario
    wait_time = between(0.1, 0.5)

class SpikeTest(ConversationAnalysisUser):
    # Spike load scenario
    wait_time = between(0, 0.1)

Automated Benchmarks

# tests/performance/benchmarks.py
import asyncio
import time
import statistics
from app.services.mcts.algorithm import MCTSAlgorithm

class PerformanceBenchmarks:
    async def benchmark_mcts_algorithm(self):
        """Benchmark MCTS algorithm performance"""
        algorithm = MCTSAlgorithm()
        test_conversation = self._create_test_conversation()
        
        times = []
        for _ in range(10):
            start = time.time()
            result = await algorithm.run(test_conversation, self._get_config())
            end = time.time()
            times.append(end - start)
        
        return {
            "mean_time": statistics.mean(times),
            "median_time": statistics.median(times),
            "p95_time": self._percentile(times, 95),
            "p99_time": self._percentile(times, 99)
        }

    async def benchmark_llm_service(self):
        """Benchmark LLM service performance"""
        # Test unified evaluation performance
        pass

    async def benchmark_cache_performance(self):
        """Benchmark semantic cache hit rates"""
        # Test cache performance under load
        pass

Implementation Steps

• Setup Locust performance testing framework
• Create realistic load test scenarios
• Implement automated benchmarking
• Add performance regression detection
• Setup performance monitoring dashboards
• Define SLA thresholds and alerts

Test Scenarios

1. Load Testing

   • Normal traffic patterns
   • Sustained load capacity
   • Resource utilization

2. Stress Testing

   • Peak traffic handling
   • Breaking point identification
   • Recovery behavior

3. Spike Testing

   • Sudden traffic spikes
   • Auto-scaling response
   • Performance degradation

Performance SLAs

• Response Time: P95 < 5 seconds for analysis
• Throughput: Support 100 concurrent users
• Error Rate: < 1% under normal load
• Availability: 99.9% uptime

Acceptance Criteria

• Comprehensive load testing suite
• Automated performance benchmarks
• Performance regression detection
• SLA validation and monitoring
• CI/CD performance gates
• Performance optimization recommendations

Effort: Medium (3-4 days)