{"version":3,"file":"regression-detector.mjs","sources":["../../../../src/lib/performance/monitoring/regression-detector.ts"],"sourcesContent":["/**\n * @file Performance Regression Detector\n * @description Statistical analysis to detect performance regressions in real-time.\n * Uses moving averages, standard deviation, and trend analysis to identify\n * significant degradations in application performance.\n *\n * Features:\n * - Statistical regression detection\n * - Baseline establishment\n * - Trend analysis\n * - Anomaly detection\n * - Alert thresholds\n * - Historical comparison\n */\n\n// ============================================================================\n// Types\n// ============================================================================\n\n/**\n * Regression severity level\n */\nexport type RegressionSeverity = 'minor' | 'moderate' | 'severe' | 'critical';\n\n/**\n * Regression status\n */\nexport type RegressionStatus = 'stable' | 'improving' | 'regressing' | 'anomaly';\n\n/**\n * Metric sample for analysis\n */\nexport interface MetricSample {\n /** Metric name */\n name: string;\n /** Metric value */\n value: number;\n /** Timestamp */\n timestamp: number;\n /** Additional context */\n context?: Record;\n}\n\n/**\n * Baseline statistics for a metric\n */\nexport interface MetricBaseline {\n /** Metric name */\n name: string;\n /** Mean value */\n mean: number;\n /** Standard deviation */\n standardDeviation: number;\n /** Median value */\n median: number;\n /** 95th percentile */\n p95: number;\n /** 99th percentile */\n p99: number;\n /** Minimum value */\n min: number;\n /** Maximum value */\n max: number;\n /** Sample count */\n sampleCount: number;\n /** Timestamp when baseline was established */\n establishedAt: number;\n /** Last updated timestamp */\n updatedAt: number;\n}\n\n/**\n * Detected regression event\n */\nexport interface RegressionEvent {\n /** Unique event ID */\n id: string;\n /** Metric name */\n metric: string;\n /** Current value */\n currentValue: number;\n /** Baseline value (mean) */\n baselineValue: number;\n /** Percentage deviation from baseline */\n deviation: number;\n /** Z-score (standard deviations from mean) */\n zScore: number;\n /** Regression severity */\n severity: RegressionSeverity;\n /** Detection timestamp */\n timestamp: number;\n /** Number of consecutive regression samples */\n consecutiveRegressions: number;\n /** Additional context */\n context?: Record;\n}\n\n/**\n * Trend analysis result\n */\nexport interface TrendAnalysis {\n /** Metric name */\n metric: string;\n /** Trend direction */\n direction: 'improving' | 'stable' | 'degrading';\n /** Trend strength (0-1) */\n strength: number;\n /** Slope of linear regression */\n slope: number;\n /** Predicted value at next interval */\n predictedNext: number;\n /** Correlation coefficient (R-squared) */\n rSquared: number;\n /** Analysis period in samples */\n periodSamples: number;\n /** Timestamp */\n timestamp: number;\n}\n\n/**\n * Anomaly detection result\n */\nexport interface AnomalyResult {\n /** Is this value an anomaly */\n isAnomaly: boolean;\n /** Anomaly score (higher = more anomalous) */\n score: number;\n /** Z-score of the value */\n zScore: number;\n /** Is it an upper outlier */\n isUpperOutlier: boolean;\n /** Is it a lower outlier */\n isLowerOutlier: boolean;\n /** Expected range */\n expectedRange: { min: number; max: number };\n}\n\n/**\n * Regression detection configuration\n */\nexport interface RegressionDetectorConfig {\n /** Enable regression detection */\n enabled: boolean;\n /** Minimum samples required for baseline */\n minBaselineSamples: number;\n /** Maximum samples to store per metric */\n maxSamplesPerMetric: number;\n /** Z-score threshold for regression (default: 2.0) */\n zScoreThreshold: number;\n /** Percentage threshold for regression (default: 0.15 = 15%) */\n percentageThreshold: number;\n /** Consecutive regressions required to trigger alert */\n consecutiveThreshold: number;\n /** Baseline recalculation interval in ms */\n baselineUpdateInterval: number;\n /** Trend analysis window size */\n trendWindowSize: number;\n /** Enable anomaly filtering */\n filterAnomalies: boolean;\n /** Anomaly score threshold */\n anomalyThreshold: number;\n /** Severity thresholds (deviation percentages) */\n severityThresholds: {\n minor: number;\n moderate: number;\n severe: number;\n critical: number;\n };\n /** Callback for regression events */\n onRegression?: (event: RegressionEvent) => void;\n /** Callback for recovery events */\n onRecovery?: (metric: string, baseline: MetricBaseline) => void;\n /** Debug mode */\n debug: boolean;\n}\n\n/**\n * Regression detector state summary\n */\nexport interface DetectorSummary {\n /** Total metrics tracked */\n metricsTracked: number;\n /** Metrics with established baselines */\n metricsWithBaselines: number;\n /** Metrics currently regressing */\n regressingMetrics: string[];\n /** Total regressions detected */\n totalRegressions: number;\n /** Recent regressions (last 24h) */\n recentRegressions: RegressionEvent[];\n /** Overall system status */\n status: RegressionStatus;\n}\n\n// ============================================================================\n// Constants\n// ============================================================================\n\nconst DEFAULT_CONFIG: RegressionDetectorConfig = {\n enabled: true,\n minBaselineSamples: 30,\n maxSamplesPerMetric: 1000,\n zScoreThreshold: 2.0,\n percentageThreshold: 0.15,\n consecutiveThreshold: 3,\n baselineUpdateInterval: 3600000, // 1 hour\n trendWindowSize: 20,\n filterAnomalies: true,\n anomalyThreshold: 3.0,\n severityThresholds: {\n minor: 0.1, // 10%\n moderate: 0.25, // 25%\n severe: 0.5, // 50%\n critical: 1.0, // 100%\n },\n debug: false,\n};\n\n// ============================================================================\n// Statistical Utilities\n// ============================================================================\n\n/**\n * Calculate mean of an array\n */\nfunction calculateMean(values: number[]): number {\n if (values.length === 0) return 0;\n return values.reduce((sum, v) => sum + v, 0) / values.length;\n}\n\n/**\n * Calculate standard deviation\n */\nfunction calculateStandardDeviation(values: number[], mean?: number): number {\n if (values.length < 2) return 0;\n const m = mean ?? calculateMean(values);\n const squaredDiffs = values.map((v) => Math.pow(v - m, 2));\n return Math.sqrt(squaredDiffs.reduce((sum, v) => sum + v, 0) / (values.length - 1));\n}\n\n/**\n * Calculate median\n */\nfunction calculateMedian(values: number[]): number {\n if (values.length === 0) return 0;\n const sorted = [...values].sort((a, b) => a - b);\n const mid = Math.floor(sorted.length / 2);\n return sorted.length % 2 === 0\n ? ((sorted[mid - 1] ?? 0) + (sorted[mid] ?? 0)) / 2\n : (sorted[mid] ?? 0);\n}\n\n/**\n * Calculate percentile\n */\nfunction calculatePercentile(values: number[], percentile: number): number {\n if (values.length === 0) return 0;\n const sorted = [...values].sort((a, b) => a - b);\n const index = (percentile / 100) * (sorted.length - 1);\n const lower = Math.floor(index);\n const upper = Math.ceil(index);\n if (lower === upper) return sorted[lower] ?? 0;\n return (sorted[lower] ?? 0) + ((sorted[upper] ?? 0) - (sorted[lower] ?? 0)) * (index - lower);\n}\n\n/**\n * Calculate Z-score\n */\nfunction calculateZScore(value: number, mean: number, stdDev: number): number {\n if (stdDev === 0) return 0;\n return (value - mean) / stdDev;\n}\n\n/**\n * Calculate linear regression slope\n */\nfunction calculateLinearRegressionSlope(values: number[]): { slope: number; rSquared: number } {\n if (values.length < 2) return { slope: 0, rSquared: 0 };\n\n const n = values.length;\n const xMean = (n - 1) / 2; // Mean of indices 0, 1, 2, ..., n-1\n const yMean = calculateMean(values);\n\n let numerator = 0;\n let denominator = 0;\n let ssTotal = 0;\n\n for (let i = 0; i < n; i++) {\n const xDiff = i - xMean;\n const yDiff = (values[i] ?? 0) - yMean;\n numerator += xDiff * yDiff;\n denominator += xDiff * xDiff;\n ssTotal += yDiff * yDiff;\n }\n\n const slope = denominator !== 0 ? numerator / denominator : 0;\n\n // Calculate R-squared\n let ssResidual = 0;\n for (let i = 0; i < n; i++) {\n const predicted = yMean + slope * (i - xMean);\n ssResidual += Math.pow((values[i] ?? 0) - predicted, 2);\n }\n\n const rSquared = ssTotal !== 0 ? 1 - ssResidual / ssTotal : 0;\n\n return { slope, rSquared: Math.max(0, Math.min(1, rSquared)) };\n}\n\n// ============================================================================\n// Regression Detector Class\n// ============================================================================\n\n/**\n * Performance regression detection engine\n */\nexport class RegressionDetector {\n private config: RegressionDetectorConfig;\n private samples: Map = new Map();\n private baselines: Map = new Map();\n private consecutiveCounts: Map = new Map();\n private regressionHistory: RegressionEvent[] = [];\n private baselineUpdateTimers: Map = new Map();\n private idCounter = 0;\n\n constructor(config: Partial = {}) {\n this.config = { ...DEFAULT_CONFIG, ...config };\n }\n\n /**\n * Record a metric sample\n */\n recordSample(sample: MetricSample): RegressionEvent | null {\n if (!this.config.enabled) return null;\n\n const { name, value, timestamp } = sample;\n\n // Initialize sample array if needed\n if (!this.samples.has(name)) {\n this.samples.set(name, []);\n }\n\n const samples = this.samples.get(name);\n if (!samples) return null;\n samples.push({ ...sample, timestamp: timestamp ?? Date.now() });\n\n // Trim samples if exceeding max\n if (samples.length > this.config.maxSamplesPerMetric) {\n samples.shift();\n }\n\n // Check if we need to establish/update baseline\n this.maybeUpdateBaseline(name);\n\n // Check for regression\n const baseline = this.baselines.get(name);\n if (!baseline) return null;\n\n // Filter anomalies if enabled\n if (this.config.filterAnomalies) {\n const anomaly = this.detectAnomaly(value, baseline);\n if (anomaly.isAnomaly) {\n this.log(`Anomaly detected for ${name}: ${value} (score: ${anomaly.score.toFixed(2)})`);\n return null;\n }\n }\n\n // Check for regression\n return this.checkRegression(name, value, baseline, sample.context);\n }\n\n /**\n * Get or establish baseline for a metric\n */\n getBaseline(metric: string): MetricBaseline | null {\n return this.baselines.get(metric) ?? null;\n }\n\n /**\n * Manually set baseline for a metric\n */\n setBaseline(metric: string, baseline: Partial): void {\n const existing = this.baselines.get(metric);\n const now = Date.now();\n\n this.baselines.set(metric, {\n name: metric,\n mean: baseline.mean ?? existing?.mean ?? 0,\n standardDeviation: baseline.standardDeviation ?? existing?.standardDeviation ?? 0,\n median: baseline.median ?? existing?.median ?? 0,\n p95: baseline.p95 ?? existing?.p95 ?? 0,\n p99: baseline.p99 ?? existing?.p99 ?? 0,\n min: baseline.min ?? existing?.min ?? 0,\n max: baseline.max ?? existing?.max ?? Infinity,\n sampleCount: baseline.sampleCount ?? existing?.sampleCount ?? 0,\n establishedAt: existing?.establishedAt ?? now,\n updatedAt: now,\n });\n }\n\n /**\n * Perform trend analysis for a metric\n */\n analyzeTrend(metric: string): TrendAnalysis | null {\n const samples = this.samples.get(metric);\n if (!samples || samples.length < this.config.trendWindowSize) {\n return null;\n }\n\n const recentSamples = samples.slice(-this.config.trendWindowSize);\n const values = recentSamples.map((s) => s.value);\n const { slope, rSquared } = calculateLinearRegressionSlope(values);\n\n const mean = calculateMean(values);\n const normalizedSlope = mean !== 0 ? slope / mean : 0;\n\n // Determine direction based on slope\n let direction: 'improving' | 'stable' | 'degrading';\n const slopeThreshold = 0.01; // 1% change per sample\n\n if (Math.abs(normalizedSlope) < slopeThreshold) {\n direction = 'stable';\n } else if (normalizedSlope < 0) {\n // Negative slope = values decreasing = improving (for most metrics)\n direction = 'improving';\n } else {\n direction = 'degrading';\n }\n\n // Calculate strength based on R-squared and slope magnitude\n const strength = Math.min(1, rSquared * Math.abs(normalizedSlope) * 10);\n\n // Predict next value\n const lastValue = values[values.length - 1];\n const predictedNext = (lastValue ?? 0) + slope;\n\n return {\n metric,\n direction,\n strength,\n slope,\n predictedNext: Math.max(0, predictedNext),\n rSquared,\n periodSamples: values.length,\n timestamp: Date.now(),\n };\n }\n\n /**\n * Detect anomalies in a value\n */\n detectAnomaly(value: number, baseline: MetricBaseline): AnomalyResult {\n const zScore = calculateZScore(value, baseline.mean, baseline.standardDeviation);\n const absZScore = Math.abs(zScore);\n\n // Calculate anomaly score (normalized)\n const score = Math.min(absZScore / this.config.anomalyThreshold, 2);\n\n // Determine expected range (3 sigma)\n const rangeMultiplier = 3;\n const expectedMin = baseline.mean - rangeMultiplier * baseline.standardDeviation;\n const expectedMax = baseline.mean + rangeMultiplier * baseline.standardDeviation;\n\n return {\n isAnomaly: absZScore > this.config.anomalyThreshold,\n score,\n zScore,\n isUpperOutlier: zScore > this.config.anomalyThreshold,\n isLowerOutlier: zScore < -this.config.anomalyThreshold,\n expectedRange: {\n min: Math.max(0, expectedMin),\n max: expectedMax,\n },\n };\n }\n\n /**\n * Get regression history\n */\n getRegressionHistory(limit?: number): RegressionEvent[] {\n const history = [...this.regressionHistory].reverse();\n return limit !== null && limit !== undefined ? history.slice(0, limit) : history;\n }\n\n /**\n * Get summary of detector state\n */\n getSummary(): DetectorSummary {\n const recentCutoff = Date.now() - 24 * 60 * 60 * 1000; // 24 hours\n const recentRegressions = this.regressionHistory.filter((r) => r.timestamp > recentCutoff);\n\n const regressingMetrics: string[] = [];\n for (const [metric, count] of this.consecutiveCounts) {\n if (count >= this.config.consecutiveThreshold) {\n regressingMetrics.push(metric);\n }\n }\n\n // Determine overall status\n let status: RegressionStatus = 'stable';\n if (regressingMetrics.length > 0) {\n status = 'regressing';\n } else {\n // Check if any metrics are improving\n let improvingCount = 0;\n for (const metric of this.baselines.keys()) {\n const trend = this.analyzeTrend(metric);\n if (trend?.direction === 'improving') {\n improvingCount++;\n }\n }\n if (improvingCount > this.baselines.size / 2) {\n status = 'improving';\n }\n }\n\n return {\n metricsTracked: this.samples.size,\n metricsWithBaselines: this.baselines.size,\n regressingMetrics,\n totalRegressions: this.regressionHistory.length,\n recentRegressions,\n status,\n };\n }\n\n /**\n * Reset detector state for a specific metric\n */\n resetMetric(metric: string): void {\n this.samples.delete(metric);\n this.baselines.delete(metric);\n this.consecutiveCounts.delete(metric);\n this.baselineUpdateTimers.delete(metric);\n }\n\n /**\n * Reset all detector state\n */\n reset(): void {\n this.samples.clear();\n this.baselines.clear();\n this.consecutiveCounts.clear();\n this.regressionHistory = [];\n this.baselineUpdateTimers.clear();\n }\n\n /**\n * Export current state for persistence\n */\n exportState(): {\n baselines: Record;\n consecutiveCounts: Record;\n } {\n return {\n baselines: Object.fromEntries(this.baselines),\n consecutiveCounts: Object.fromEntries(this.consecutiveCounts),\n };\n }\n\n /**\n * Import persisted state\n */\n importState(state: {\n baselines?: Record;\n consecutiveCounts?: Record;\n }): void {\n if (state.baselines) {\n this.baselines = new Map(Object.entries(state.baselines));\n }\n if (state.consecutiveCounts) {\n this.consecutiveCounts = new Map(Object.entries(state.consecutiveCounts));\n }\n }\n\n // ============================================================================\n // Private Methods\n // ============================================================================\n\n private maybeUpdateBaseline(metric: string): void {\n const samples = this.samples.get(metric);\n if (!samples) return;\n\n const existing = this.baselines.get(metric);\n const now = Date.now();\n\n // Check if we should update\n const shouldUpdate =\n !existing ||\n (samples.length >= this.config.minBaselineSamples &&\n now - (existing?.updatedAt ?? 0) > this.config.baselineUpdateInterval);\n\n if (!shouldUpdate) return;\n if (samples.length < this.config.minBaselineSamples) return;\n\n // Calculate new baseline\n const values = samples.map((s) => s.value);\n const mean = calculateMean(values);\n const standardDeviation = calculateStandardDeviation(values, mean);\n const median = calculateMedian(values);\n const p95 = calculatePercentile(values, 95);\n const p99 = calculatePercentile(values, 99);\n const min = Math.min(...values);\n const max = Math.max(...values);\n\n const baseline: MetricBaseline = {\n name: metric,\n mean,\n standardDeviation,\n median,\n p95,\n p99,\n min,\n max,\n sampleCount: values.length,\n establishedAt: existing?.establishedAt ?? now,\n updatedAt: now,\n };\n\n this.baselines.set(metric, baseline);\n this.log(\n `Baseline updated for ${metric}: mean=${mean.toFixed(2)}, stdDev=${standardDeviation.toFixed(2)}`\n );\n }\n\n private checkRegression(\n metric: string,\n value: number,\n baseline: MetricBaseline,\n context?: Record\n ): RegressionEvent | null {\n const zScore = calculateZScore(value, baseline.mean, baseline.standardDeviation);\n const deviation = baseline.mean !== 0 ? (value - baseline.mean) / baseline.mean : 0;\n\n // Check if this is a regression (higher values are worse for most metrics)\n const isRegression =\n zScore > this.config.zScoreThreshold || deviation > this.config.percentageThreshold;\n\n if (isRegression) {\n // Increment consecutive count\n const currentCount = (this.consecutiveCounts.get(metric) ?? 0) + 1;\n this.consecutiveCounts.set(metric, currentCount);\n\n // Only create event if we've hit the threshold\n if (currentCount >= this.config.consecutiveThreshold) {\n const severity = this.calculateSeverity(deviation);\n\n const event: RegressionEvent = {\n id: this.generateId(),\n metric,\n currentValue: value,\n baselineValue: baseline.mean,\n deviation: deviation * 100, // Convert to percentage\n zScore,\n severity,\n timestamp: Date.now(),\n consecutiveRegressions: currentCount,\n context,\n };\n\n this.regressionHistory.push(event);\n this.config.onRegression?.(event);\n this.log(`Regression detected for ${metric}: ${(deviation * 100).toFixed(1)}% deviation`);\n\n return event;\n }\n } else {\n // Reset consecutive count on recovery\n const previousCount = this.consecutiveCounts.get(metric) ?? 0;\n if (previousCount >= this.config.consecutiveThreshold) {\n this.config.onRecovery?.(metric, baseline);\n this.log(`Recovery detected for ${metric}`);\n }\n this.consecutiveCounts.set(metric, 0);\n }\n\n return null;\n }\n\n private calculateSeverity(deviation: number): RegressionSeverity {\n const absDeviation = Math.abs(deviation);\n const thresholds = this.config.severityThresholds;\n\n if (absDeviation >= thresholds.critical) return 'critical';\n if (absDeviation >= thresholds.severe) return 'severe';\n if (absDeviation >= thresholds.moderate) return 'moderate';\n return 'minor';\n }\n\n private generateId(): string {\n return `reg-${Date.now()}-${++this.idCounter}`;\n }\n\n private log(message: string, ...args: unknown[]): void {\n if (this.config.debug) {\n console.info(`[RegressionDetector] ${message}`, ...args);\n }\n }\n}\n\n// ============================================================================\n// Singleton Instance\n// ============================================================================\n\nlet detectorInstance: RegressionDetector | null = null;\n\n/**\n * Get or create the global regression detector\n */\nexport function getRegressionDetector(\n config?: Partial\n): RegressionDetector {\n detectorInstance ??= new RegressionDetector(config);\n return detectorInstance;\n}\n\n/**\n * Reset the detector instance\n */\nexport function resetRegressionDetector(): void {\n detectorInstance?.reset();\n detectorInstance = null;\n}\n\n// ============================================================================\n// Convenience Functions\n// ============================================================================\n\n/**\n * Record a performance sample\n */\nexport function recordPerformanceSample(\n metric: string,\n value: number,\n context?: Record\n): RegressionEvent | null {\n return getRegressionDetector().recordSample({\n name: metric,\n value,\n timestamp: Date.now(),\n context,\n });\n}\n\n/**\n * Analyze performance trend for a metric\n */\nexport function analyzePerformanceTrend(metric: string): TrendAnalysis | null {\n return getRegressionDetector().analyzeTrend(metric);\n}\n\n/**\n * Get current regression summary\n */\nexport function getRegressionSummary(): DetectorSummary {\n return 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