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AIOSDNA

testing_strategy

AIOSx Testing Strategy

Overview

This document outlines the testing strategy for AIOSx Kernel Mesh, covering unit, integration, chaos, and end-to-end testing approaches.

Testing Pyramid

        /\
       /  \     E2E Tests (Few)
      /____\
     /      \   Integration Tests (Some)
    /________\
   /          \  Unit Tests (Many)
  /____________\

1. Unit Tests

Purpose

Test individual components in isolation with mocked dependencies.

Coverage Areas

VoiceOS Kernel

  • CallSessionManager: Session lifecycle, state transitions, timeout logic
  • AudioStreamHandler: Audio frame buffering, jitter handling, backpressure
  • DialogOrchestrator: NLU interpretation, LLM integration, dialog state management
  • Provider Abstractions: STT/TTS/NLU provider fallback logic

Test File: tests/unit/test_voiceos_call_session.py

Security/SentinelX Kernel

  • SecurityEventIngestor: Event normalization, context enrichment
  • DetectionEngine: Rule evaluation, threshold detection, anomaly detection
  • ResponseEngine: Action execution, target extraction, error handling

Test File: tests/unit/test_security_detection.py

Mesh Components

  • KernelRegistry: Registration, heartbeat, querying, cleanup
  • Mesh Orchestrator: Routing policies, node selection, fallback logic
  • Mesh Circuit Breaker: Isolation, recovery, backup routing

AKO Components

  • AKOController: Observation collection, action generation, execution
  • TimeSeriesFeatureBuilder: EWMA calculation, window aggregation, anomaly signals
  • Optimizers: Rule-based logic, UCB bandit algorithm, canary rollout

Best Practices

  • Use pytest fixtures for test setup
  • Mock external dependencies
  • Test edge cases (timeouts, failures, invalid inputs)
  • Aim for >80% code coverage

Example

python
@pytest.mark.asyncio
async def test_session_state_transitions():
    manager = CallSessionManager()
    session = await manager.create_session("tenant_1")
    
    # Test valid transition
    success = await manager.transition_state(
        session.session_id,
        CallSessionState.ACTIVE,
    )
    assert success is True
    
    # Test invalid transition
    success = await manager.transition_state(
        session.session_id,
        CallSessionState.INIT,  # Can't go back
    )
    assert success is False

2. Integration Tests

Purpose

Test interactions between multiple components with real (or realistic) dependencies.

Coverage Areas

Workflow Execution

  • Elite Trade Flow: Complete flow from signal to execution to explanation
  • VoiceOS Support Flow: Call session with STT, NLU, LLM, TTS
  • Cross-Kernel Workflows: Multi-kernel interactions

Test File: tests/integration/test_elite_trade_flow.py

Domain Kernel Integration

  • VoiceOS Flow: Session creation → Audio processing → Dialog → Response
  • Security Flow: Event ingestion → Detection → Response execution
  • DeFi-FX Flow: Market data → Strategy → Risk → Execution

Test File: tests/integration/test_voiceos_flow.py

Chaos & Resilience

  • Chaos Scenarios: Execute chaos experiments, verify self-healing
  • Resilience Metrics: Detection latency, recovery time, SLO adherence
  • Failover Testing: Node failures, network partitions, service degradation

Test File: tests/integration/test_chaos_resilience.py

Best Practices

  • Use test databases (SQLite for speed)
  • Clean up test data after each test
  • Test both success and failure paths
  • Verify side effects (database writes, API calls)

Example

python
@pytest.mark.asyncio
async def test_elite_trade_flow_execution():
    engine = WorkflowEngine()
    # Register mock kernel clients
    engine.register_kernel_client("trading", MockTradingKernel())
    
    trade_flow = EliteTradeFlow(engine)
    context = await trade_flow.execute("BTC/USD", 1.0)
    
    assert context.execution_id is not None
    assert context.security_approved is True
    assert context.risk_approved is True

3. Chaos Testing

Purpose

Validate system resilience under failure conditions.

Chaos Scenarios

Trading Kernel

  • API Timeout: Simulate trading API timeouts
  • Expected: Self-healing, failover to backup, SLO maintained

DeFi-FX Kernel

  • RPC Failure: Simulate blockchain RPC failures
  • Expected: Venue failover, error handling, graceful degradation

LLM Kernel

  • Endpoint Saturation: Simulate LLM endpoint overload
  • Expected: Load balancing, model fallback, throttling

VoiceOS Kernel

  • Packet Loss: Simulate network packet loss
  • Expected: Provider fallback, quality degradation, session recovery

SentinelX Kernel

  • Event Storm: Simulate security event flood
  • Expected: Rate limiting, batch processing, detection accuracy

Resilience Metrics

  1. Detection Latency (MTTD): Time to detect failure
  2. Recovery Latency (MTTR): Time to recover from failure
  3. Workflow Success Rate: % of workflows completing successfully
  4. SLO Violations: Number and severity of SLO violations
  5. AKO Response Effectiveness: How well AKO responds to chaos

Resilience Score Formula

resilience_score = (
    detection_score * 0.2 +
    recovery_score * 0.3 +
    workflow_score * 0.3 +
    slo_score * 0.1 +
    ako_score * 0.1
)

Best Practices

  • Run chaos tests in staging environment
  • Start with low-impact scenarios
  • Gradually increase severity
  • Monitor all metrics during experiments
  • Document findings and improvements

4. End-to-End Tests

Purpose

Test complete system behavior from user perspective.

Test Suite Structure

Smoke Tests

  • System connectivity
  • Kernel registration
  • Health checks
  • Basic workflow execution

Test File: tests/e2e/test_smoke_tests.py

Critical Workflow Tests

  • Elite Trade Flow (Trading → DeFi-FX → LLM → Security)
  • VoiceOS Support Flow (Call → STT → LLM → TTS)
  • Manufacturing Decision Flow
  • Security Investigation Flow

Chaos Experiments

  • Selected chaos scenarios
  • Verify self-healing
  • Check SLO adherence

KPI Baseline Checks

  • Verify business KPIs are tracked
  • Check ROI attribution
  • Validate metrics export

E2E Test Runner

python
def test_e2e_suite():
    """Run complete E2E test suite"""
    results = {
        "smoke_tests": run_smoke_tests(),
        "workflow_tests": run_workflow_tests(),
        "chaos_tests": run_chaos_experiments(),
        "kpi_checks": run_kpi_baseline_checks(),
    }
    
    # Generate report
    return generate_report(results)

5. Regression Tests

Purpose

Ensure new changes don't break existing functionality.

Template for New Domain Kernels

python
@pytest.mark.asyncio
async def test_domain_kernel_template():
    """Regression test template for new domain kernels"""
    kernel = DomainKernel()
    
    # Test initialization
    await kernel.initialize()
    assert kernel.kernel_id is not None
    
    # Test lifecycle
    await kernel.start()
    assert kernel._is_running is True
    
    # Test domain-specific operations
    result = await kernel.process({"input": "data"})
    assert result is not None
    
    # Test health
    health = await kernel.get_domain_specific_health()
    assert "perception_status" in health
    
    # Test cleanup
    await kernel.stop()
    assert kernel._is_running is False

6. Performance Tests

Purpose

Validate system performance under load.

Test Scenarios

  • Load Testing: Gradual increase in request rate
  • Stress Testing: System behavior at capacity limits
  • Spike Testing: Sudden traffic spikes
  • Endurance Testing: Long-running stability

Metrics to Track

  • Request latency (p50, p95, p99)
  • Throughput (requests per second)
  • Error rate
  • Resource utilization (CPU, memory)
  • Queue backlog

7. Security Tests

Purpose

Validate security controls and threat detection.

Test Scenarios

  • Authentication: Failed login attempts, token validation
  • Authorization: Access control, permission checks
  • Threat Detection: Anomaly detection, rule-based detection
  • Response Actions: Isolation, throttling, blocking

Testing Checklist

For Each Domain Team

Before Code Review

  • Unit tests written for new code
  • Integration tests for cross-kernel interactions
  • Edge cases covered
  • Error handling tested
  • Performance implications considered

Before Deployment

  • All unit tests pass
  • Integration tests pass
  • Smoke tests pass
  • No critical linter errors
  • Documentation updated

After Deployment

  • Monitor production metrics
  • Run chaos experiments (staging)
  • Verify SLO adherence
  • Check business KPIs

Continuous Integration

Test Execution Order

  1. Linting: Code style and static analysis
  2. Unit Tests: Fast, isolated component tests
  3. Integration Tests: Component interaction tests
  4. E2E Tests: Full system tests (can be run less frequently)

Test Environments

  • CI/CD: Run all tests on every commit
  • Staging: Run E2E and chaos tests before release
  • Production: Monitor metrics, run smoke tests

Test Data Management

Best Practices

  • Use fixtures for test data
  • Clean up after tests
  • Use separate test databases
  • Mock external services
  • Use deterministic test data

Coverage Goals

  • Unit Tests: >80% code coverage
  • Integration Tests: Cover all critical workflows
  • E2E Tests: Cover all user-facing features
  • Chaos Tests: Cover all failure scenarios

Tools

  • pytest: Test framework
  • pytest-asyncio: Async test support
  • pytest-cov: Coverage reporting
  • pytest-mock: Mocking support
  • Chaos Toolkit: Chaos engineering

Running Tests

bash
# Run all tests
pytest tests/


# Run unit tests only
pytest tests/unit/


# Run integration tests only
pytest tests/integration/


# Run with coverage
pytest --cov=aiosx tests/


# Run specific test file
pytest tests/unit/test_voiceos_call_session.py


# Run with verbose output
pytest -v tests/

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