Michael Schiemer
3b623e7afb
- Create AnsibleDeployStage using framework's Process module for secure command execution - Integrate AnsibleDeployStage into DeploymentPipelineCommands for production deployments - Add force_deploy flag support in Ansible playbook to override stale locks - Use PHP deployment module as orchestrator (php console.php deploy:production) - Fix ErrorAggregationInitializer to use Environment class instead of $_ENV superglobal Architecture: - BuildStage → AnsibleDeployStage → HealthCheckStage for production - Process module provides timeout, error handling, and output capture - Ansible playbook supports rollback via rollback-git-based.yml - Zero-downtime deployments with health checks
272 lines
12 KiB
PHP
272 lines
12 KiB
PHP
<?php
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declare(strict_types=1);
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/**
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* ML Adapter Integration Tests
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*
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* Tests all 3 ML adapters with ModelRegistry and ModelPerformanceMonitor:
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* 1. QueueAnomalyModelAdapter
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* 2. WafBehavioralModelAdapter
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* 3. NPlusOneModelAdapter
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*/
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require_once __DIR__ . '/../../vendor/autoload.php';
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use App\Framework\Queue\MachineLearning\QueueAnomalyModelAdapter;
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use App\Framework\Queue\MachineLearning\JobAnomalyDetector;
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use App\Framework\Queue\MachineLearning\ValueObjects\JobFeatures;
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use App\Framework\Waf\MachineLearning\WafBehavioralModelAdapter;
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use App\Framework\Waf\MachineLearning\BehaviorAnomalyDetector;
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use App\Framework\Waf\MachineLearning\ValueObjects\BehaviorFeatures;
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use App\Framework\Database\NPlusOneDetection\MachineLearning\NPlusOneModelAdapter;
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use App\Framework\Database\NPlusOneDetection\MachineLearning\NPlusOneDetectionEngine;
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use App\Framework\MachineLearning\ModelManagement\InMemoryModelRegistry;
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use App\Framework\MachineLearning\ModelManagement\ModelPerformanceMonitor;
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use App\Framework\MachineLearning\ModelManagement\InMemoryPerformanceStorage;
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use App\Framework\MachineLearning\ModelManagement\NullAlertingService;
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use App\Framework\Core\ValueObjects\Score;
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echo "=== ML Adapter Integration Tests ===\n\n";
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try {
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// Initialize shared infrastructure
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echo "1. Initializing Model Management Infrastructure...\n";
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$registry = new InMemoryModelRegistry();
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$storage = new InMemoryPerformanceStorage();
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$alerting = new NullAlertingService();
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$performanceMonitor = new ModelPerformanceMonitor($registry, $storage, $alerting);
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echo " ✓ ModelRegistry created\n";
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echo " ✓ PerformanceStorage created\n";
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echo " ✓ ModelPerformanceMonitor created\n\n";
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// ========================================================================
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// Test 1: QueueAnomalyModelAdapter
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// ========================================================================
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echo "2. Testing QueueAnomalyModelAdapter...\n";
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// Create detector and adapter
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$queueDetector = new JobAnomalyDetector(
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anomalyThreshold: new Score(0.4),
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zScoreThreshold: 3.0,
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iqrMultiplier: 1.5
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);
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$queueAdapter = new QueueAnomalyModelAdapter(
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$registry,
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$performanceMonitor,
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$queueDetector
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);
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// Register model
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echo " → Registering queue-anomaly model...\n";
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$queueMetadata = $queueAdapter->registerCurrentModel();
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echo " ✓ Model registered: {$queueMetadata->modelName} v{$queueMetadata->version->toString()}\n";
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// Test with normal features
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echo " → Testing with normal job features...\n";
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$normalFeatures = new JobFeatures(
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executionTimeVariance: 0.15,
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memoryUsagePattern: 0.10,
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retryFrequency: 0.0,
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failureRate: 0.05,
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queueDepthCorrelation: 0.10,
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dependencyChainComplexity: 0.08,
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payloadSizeAnomaly: 0.05,
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executionTimingRegularity: 0.30
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);
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$normalResult = $queueAdapter->analyzeWithTracking($normalFeatures, groundTruth: false);
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echo " ✓ Analysis: " . ($normalResult['is_anomalous'] ? "ANOMALOUS" : "NORMAL") . "\n";
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echo " ✓ Score: " . sprintf("%.2f%%", $normalResult['anomaly_score'] * 100) . "\n";
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echo " ✓ Tracking: {$normalResult['tracking']['prediction']} (ground truth: false)\n";
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// Test with anomalous features
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echo " → Testing with anomalous job features...\n";
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$anomalousFeatures = new JobFeatures(
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executionTimeVariance: 0.85,
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memoryUsagePattern: 0.75,
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retryFrequency: 0.85,
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failureRate: 0.65,
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queueDepthCorrelation: 0.50,
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dependencyChainComplexity: 0.30,
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payloadSizeAnomaly: 0.35,
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executionTimingRegularity: 0.20
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);
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$anomalousResult = $queueAdapter->analyzeWithTracking($anomalousFeatures, groundTruth: true);
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echo " ✓ Analysis: " . ($anomalousResult['is_anomalous'] ? "ANOMALOUS" : "NORMAL") . "\n";
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echo " ✓ Score: " . sprintf("%.2f%%", $anomalousResult['anomaly_score'] * 100) . "\n";
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echo " ✓ Tracking: {$anomalousResult['tracking']['prediction']} (ground truth: true)\n";
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// Get performance metrics
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echo " → Checking performance metrics...\n";
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$queueMetrics = $queueAdapter->getCurrentPerformanceMetrics();
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echo " ✓ Total predictions: {$queueMetrics['total_predictions']}\n";
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echo " ✓ Accuracy: " . sprintf("%.2f%%", $queueMetrics['accuracy'] * 100) . "\n\n";
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// ========================================================================
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// Test 2: WafBehavioralModelAdapter
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// ========================================================================
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echo "3. Testing WafBehavioralModelAdapter...\n";
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// Create detector and adapter
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$wafDetector = new BehaviorAnomalyDetector(
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anomalyThreshold: new Score(0.5),
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zScoreThreshold: 2.5,
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iqrMultiplier: 1.5
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);
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$wafAdapter = new WafBehavioralModelAdapter(
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$registry,
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$performanceMonitor,
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$wafDetector
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);
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// Register model
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echo " → Registering waf-behavioral model...\n";
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$wafMetadata = $wafAdapter->registerCurrentModel();
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echo " ✓ Model registered: {$wafMetadata->modelName} v{$wafMetadata->version->toString()}\n";
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// Test with benign request
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echo " → Testing with benign request features...\n";
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$benignFeatures = new BehaviorFeatures(
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requestFrequency: 0.2,
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endpointDiversity: 2.5, // Moderate diversity
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parameterEntropy: 3.0, // Normal entropy
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userAgentConsistency: 0.9, // Consistent UA
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geographicAnomaly: 0.1, // Same location
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timePatternRegularity: 0.3, // Human-like timing
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payloadSimilarity: 0.4, // Varied payloads
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httpMethodDistribution: 0.6 // Mixed methods
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);
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$benignResult = $wafAdapter->analyzeWithTracking($benignFeatures, historicalBaseline: [], groundTruth: false);
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echo " ✓ Analysis: " . ($benignResult['is_anomalous'] ? "MALICIOUS" : "BENIGN") . "\n";
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echo " ✓ Score: " . sprintf("%.2f%%", $benignResult['anomaly_score'] * 100) . "\n";
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echo " ✓ Tracking: {$benignResult['tracking']['prediction']} (ground truth: false)\n";
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// Test with malicious request
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echo " → Testing with malicious request features...\n";
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$maliciousFeatures = new BehaviorFeatures(
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requestFrequency: 20.0, // Very high frequency (>10/s)
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endpointDiversity: 0.5, // Low diversity (scanning)
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parameterEntropy: 7.0, // High entropy (probing)
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userAgentConsistency: 0.1, // Inconsistent UA
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geographicAnomaly: 0.85, // Suspicious location changes
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timePatternRegularity: 0.95, // Automated timing
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payloadSimilarity: 0.9, // Repetitive payloads
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httpMethodDistribution: 0.2 // Limited methods
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);
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$maliciousResult = $wafAdapter->analyzeWithTracking($maliciousFeatures, historicalBaseline: [], groundTruth: true);
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echo " ✓ Analysis: " . ($maliciousResult['is_anomalous'] ? "MALICIOUS" : "BENIGN") . "\n";
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echo " ✓ Score: " . sprintf("%.2f%%", $maliciousResult['anomaly_score'] * 100) . "\n";
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echo " ✓ Tracking: {$maliciousResult['tracking']['prediction']} (ground truth: true)\n";
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// Get performance metrics
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echo " → Checking performance metrics...\n";
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$wafMetrics = $wafAdapter->getCurrentPerformanceMetrics();
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echo " ✓ Total predictions: {$wafMetrics['total_predictions']}\n";
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echo " ✓ Accuracy: " . sprintf("%.2f%%", $wafMetrics['accuracy'] * 100) . "\n\n";
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// ========================================================================
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// Test 3: NPlusOneModelAdapter
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// ========================================================================
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echo "4. Testing NPlusOneModelAdapter...\n";
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echo " ℹ️ Requires QueryExecutionContext and full NPlusOneDetectionEngine\n";
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echo " ℹ️ Skipping for now (database-dependent)\n\n";
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// ========================================================================
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// Model Registry Tests
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// ========================================================================
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echo "5. Testing ModelRegistry Integration...\n";
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// List all registered models
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echo " → Listing registered models...\n";
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$modelNames = $registry->getAllModelNames();
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echo " ✓ Total model types registered: " . count($modelNames) . "\n";
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foreach ($modelNames as $modelName) {
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$versions = $registry->getAll($modelName);
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foreach ($versions as $metadata) {
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echo " - {$metadata->modelName} v{$metadata->version->toString()}\n";
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echo " Type: {$metadata->modelType->value}\n";
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echo " Created: {$metadata->createdAt->format('Y-m-d H:i:s')}\n";
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}
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}
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// Test model existence
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echo " → Testing model existence checks...\n";
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$queueExists = $registry->exists('queue-anomaly', \App\Framework\Core\ValueObjects\Version::fromString('1.0.0'));
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$wafExists = $registry->exists('waf-behavioral', \App\Framework\Core\ValueObjects\Version::fromString('1.0.0'));
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echo " ✓ queue-anomaly exists: " . ($queueExists ? "YES" : "NO") . "\n";
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echo " ✓ waf-behavioral exists: " . ($wafExists ? "YES" : "NO") . "\n\n";
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// ========================================================================
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// Performance Monitor Tests
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// ========================================================================
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echo "6. Testing ModelPerformanceMonitor Integration...\n";
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// Get metrics for each registered model
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echo " → Getting metrics for all registered models...\n";
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$allMetrics = [];
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foreach ($modelNames as $modelName) {
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$versions = $registry->getAll($modelName);
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foreach ($versions as $metadata) {
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$metrics = $performanceMonitor->getCurrentMetrics(
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$metadata->modelName,
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$metadata->version
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);
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$modelKey = "{$metadata->modelName}@{$metadata->version->toString()}";
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$allMetrics[$modelKey] = $metrics;
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}
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}
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echo " ✓ Models tracked: " . count($allMetrics) . "\n";
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foreach ($allMetrics as $modelKey => $metrics) {
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echo " - $modelKey:\n";
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echo " Predictions: {$metrics['total_predictions']}\n";
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echo " Accuracy: " . sprintf("%.2f%%", $metrics['accuracy'] * 100) . "\n";
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if ($metrics['total_predictions'] > 0) {
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echo " Avg Confidence: " . sprintf("%.2f%%", $metrics['average_confidence'] * 100) . "\n";
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}
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}
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// Check for performance degradation
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echo "\n → Checking for performance degradation...\n";
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$queueDegradation = $queueAdapter->checkPerformanceDegradation(0.05);
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$wafDegradation = $wafAdapter->checkPerformanceDegradation(0.05);
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echo " ✓ queue-anomaly degraded: " . ($queueDegradation['has_degraded'] ? "YES" : "NO") . "\n";
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echo " ✓ waf-behavioral degraded: " . ($wafDegradation['has_degraded'] ? "YES" : "NO") . "\n\n";
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// ========================================================================
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// Test Summary
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// ========================================================================
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echo "=== Test Summary ===\n";
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echo "✓ QueueAnomalyModelAdapter: Working\n";
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echo "✓ WafBehavioralModelAdapter: Working\n";
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echo "✓ NPlusOneModelAdapter: Skipped (database-dependent)\n";
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echo "✓ ModelRegistry: Working\n";
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echo "✓ ModelPerformanceMonitor: Working\n";
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echo "✓ Model registration: Working\n";
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echo "✓ Performance tracking: Working\n";
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echo "✓ Accuracy calculation: Working\n\n";
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echo "Test Results:\n";
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echo " - Queue Adapter: 2 predictions, " . sprintf("%.0f%%", $queueMetrics['accuracy'] * 100) . " accuracy\n";
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echo " - WAF Adapter: 2 predictions, " . sprintf("%.0f%%", $wafMetrics['accuracy'] * 100) . " accuracy\n";
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echo " - Total models registered: " . $registry->getTotalCount() . "\n";
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echo " - Total predictions tracked: " . array_sum(array_column($allMetrics, 'total_predictions')) . "\n\n";
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echo "=== ML Adapter Tests PASSED ===\n";
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} catch (\Throwable $e) {
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echo "\n!!! TEST FAILED !!!\n";
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echo "Error: " . $e->getMessage() . "\n";
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echo "File: " . $e->getFile() . ":" . $e->getLine() . "\n";
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echo "\nStack trace:\n" . $e->getTraceAsString() . "\n";
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exit(1);
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}
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