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BaselineAnalysis,
GCAnalysis,
GCEvent,
LeakAnalysis,
LeakProbabilityFactors,
LeakSensitivity,
MemoryInfo,
Trend,
} from "../types";
import {
BASELINE_GROWTH_THRESHOLD,
GC_DETECTION_THRESHOLD,
LEAK_PROBABILITY_THRESHOLD,
LEAK_SENSITIVITY_CONFIG,
MIN_LEAK_DETECTION_SAMPLES,
} from "../constants";
/**
* Result of linear regression analysis
*/
export interface RegressionResult {
/** Slope of the regression line (bytes per sample) */
slope: number;
/** Y-intercept of the regression line */
intercept: number;
/** R-squared value (coefficient of determination, 0-1) */
rSquared: number;
}
/**
* Perform simple linear regression on a set of points.
* Uses the least squares method.
*
* @param points - Array of [x, y] coordinate pairs
* @returns Regression result with slope, intercept, and R-squared
*/
export function linearRegression(points: [number, number][]): RegressionResult {
const n = points.length;
if (n < 2) {
return { slope: 0, intercept: 0, rSquared: 0 };
}
// Calculate sums
let sumX = 0;
let sumY = 0;
let sumXY = 0;
let sumX2 = 0;
let sumY2 = 0;
for (const [x, y] of points) {
sumX += x;
sumY += y;
sumXY += x * y;
sumX2 += x * x;
sumY2 += y * y;
}
// Calculate slope and intercept
const denominator = n * sumX2 - sumX * sumX;
Iif (denominator === 0) {
return { slope: 0, intercept: sumY / n, rSquared: 0 };
}
const slope = (n * sumXY - sumX * sumY) / denominator;
const intercept = (sumY - slope * sumX) / n;
// Calculate R-squared (coefficient of determination)
const meanY = sumY / n;
let ssTotal = 0; // Total sum of squares
let ssResidual = 0; // Residual sum of squares
for (const [x, y] of points) {
const predicted = slope * x + intercept;
ssTotal += Math.pow(y - meanY, 2);
ssResidual += Math.pow(y - predicted, 2);
}
const rSquared = ssTotal === 0 ? 0 : 1 - ssResidual / ssTotal;
return {
slope,
intercept,
rSquared: Math.max(0, Math.min(1, rSquared)), // Clamp to [0, 1]
};
}
/**
* Calculate the memory trend from samples.
*
* @param samples - Array of memory info samples
* @returns Trend direction
*/
export function calculateTrend(samples: MemoryInfo[]): Trend {
if (samples.length < 2) {
return "stable";
}
// Convert samples to [index, heapUsed] points
const points: [number, number][] = samples.map((s, i) => [i, s.heapUsed]);
const { slope } = linearRegression(points);
// Normalize slope by average heap size for relative comparison
const avgHeap = samples.reduce((sum, s) => sum + s.heapUsed, 0) / samples.length;
const normalizedSlope = avgHeap > 0 ? slope / avgHeap : 0;
// Use stricter thresholds
if (normalizedSlope > 0.02) {
return "increasing";
}
if (normalizedSlope < -0.02) {
return "decreasing";
}
return "stable";
}
/**
* Calculate average growth rate (bytes per sample).
*
* @param samples - Array of memory info samples
* @returns Average growth rate in bytes per sample
*/
export function calculateAverageGrowth(samples: MemoryInfo[]): number {
if (samples.length < 2) {
return 0;
}
const points: [number, number][] = samples.map((s, i) => [i, s.heapUsed]);
const { slope } = linearRegression(points);
return slope;
}
/**
* Detect GC events in memory samples.
* A GC event is detected when memory drops significantly between samples.
*
* @param samples - Array of memory info samples
* @param threshold - Minimum drop ratio to consider as GC (default: 0.10 = 10%)
* @returns GC analysis result
*/
export function detectGCEvents(
samples: MemoryInfo[],
threshold: number = GC_DETECTION_THRESHOLD
): GCAnalysis {
const gcEvents: GCEvent[] = [];
Iif (samples.length < 2) {
return {
gcEventCount: 0,
gcEvents: [],
avgRecoveryRatio: 0,
lastGCTimestamp: null,
isGCEffective: false,
};
}
for (let i = 1; i < samples.length; i++) {
const prev = samples[i - 1];
const curr = samples[i];
const drop = prev.heapUsed - curr.heapUsed;
const dropRatio = drop / prev.heapUsed;
// Significant drop indicates GC
if (dropRatio >= threshold) {
gcEvents.push({
sampleIndex: i,
memoryBefore: prev.heapUsed,
memoryAfter: curr.heapUsed,
recoveryAmount: drop,
recoveryRatio: dropRatio,
timestamp: curr.timestamp,
});
}
}
const gcEventCount = gcEvents.length;
const avgRecoveryRatio = gcEventCount > 0
? gcEvents.reduce((sum, e) => sum + e.recoveryRatio, 0) / gcEventCount
: 0;
const lastGCTimestamp = gcEventCount > 0
? gcEvents[gcEvents.length - 1].timestamp
: null;
// GC is effective if it recovers at least 15% on average
const isGCEffective = avgRecoveryRatio >= 0.15;
return {
gcEventCount,
gcEvents,
avgRecoveryRatio,
lastGCTimestamp,
isGCEffective,
};
}
/**
* Calculate baseline from post-GC memory values.
* Baseline represents the "floor" memory after GC cycles.
*
* @param samples - Array of memory info samples
* @param gcEvents - Detected GC events
* @returns Baseline analysis
*/
export function calculateBaseline(
samples: MemoryInfo[],
gcEvents: GCEvent[]
): BaselineAnalysis {
Iif (samples.length === 0) {
return {
baselineHeap: 0,
currentHeap: 0,
growthFromBaseline: 0,
growthRatio: 0,
isBaselineEstablished: false,
isSignificantGrowth: false,
};
}
const currentHeap = samples[samples.length - 1].heapUsed;
// If we have GC events, use post-GC values as baseline
if (gcEvents.length >= 2) {
const postGCValues = gcEvents.map(e => e.memoryAfter);
const baselineHeap = postGCValues.reduce((sum, v) => sum + v, 0) / postGCValues.length;
const growthFromBaseline = currentHeap - baselineHeap;
const growthRatio = baselineHeap > 0 ? growthFromBaseline / baselineHeap : 0;
return {
baselineHeap,
currentHeap,
growthFromBaseline,
growthRatio,
isBaselineEstablished: true,
isSignificantGrowth: growthRatio > BASELINE_GROWTH_THRESHOLD,
};
}
// Without enough GC events, use minimum value as approximate baseline
const minHeap = Math.min(...samples.map(s => s.heapUsed));
const growthFromBaseline = currentHeap - minHeap;
const growthRatio = minHeap > 0 ? growthFromBaseline / minHeap : 0;
return {
baselineHeap: minHeap,
currentHeap,
growthFromBaseline,
growthRatio,
isBaselineEstablished: false,
isSignificantGrowth: growthRatio > BASELINE_GROWTH_THRESHOLD,
};
}
/**
* Analyze trend of post-GC baselines.
* If baselines are increasing over time, it indicates a true leak.
*
* @param gcEvents - Detected GC events
* @returns Trend of baseline growth
*/
export function analyzeBaselineTrend(gcEvents: GCEvent[]): {
trend: Trend;
slope: number;
rSquared: number;
} {
if (gcEvents.length < 2) {
return { trend: "stable", slope: 0, rSquared: 0 };
}
// Use post-GC values indexed by event order
const points: [number, number][] = gcEvents.map((e, i) => [i, e.memoryAfter]);
const { slope, rSquared } = linearRegression(points);
// Normalize by average post-GC memory
const avgPostGC = gcEvents.reduce((sum, e) => sum + e.memoryAfter, 0) / gcEvents.length;
const normalizedSlope = avgPostGC > 0 ? slope / avgPostGC : 0;
let trend: Trend = "stable";
Iif (normalizedSlope > 0.02) {
trend = "increasing";
I} else if (normalizedSlope < -0.02) {
trend = "decreasing";
}
return { trend, slope, rSquared };
}
/**
* Calculate observation time from samples
*/
function calculateObservationTime(samples: MemoryInfo[]): number {
Iif (samples.length < 2) return 0;
return samples[samples.length - 1].timestamp - samples[0].timestamp;
}
/**
* Generate a human-readable recommendation based on leak analysis.
*
* @param probability - Leak probability (0-100)
* @param trend - Memory trend
* @param averageGrowth - Average growth rate in bytes
* @param gcAnalysis - GC analysis results
* @returns Recommendation string
*/
export function generateRecommendation(
probability: number,
trend: Trend,
averageGrowth: number,
gcAnalysis?: GCAnalysis
): string | undefined {
if (probability < 30) {
return undefined;
}
const gcInfo = gcAnalysis && gcAnalysis.gcEventCount > 0
? ` GC has occurred ${gcAnalysis.gcEventCount} time(s) with ${(gcAnalysis.avgRecoveryRatio * 100).toFixed(0)}% average recovery.`
: "";
if (probability >= 80) {
return `Critical: High probability of memory leak detected. Memory is growing at ${formatGrowthRate(averageGrowth)} per sample even after GC.${gcInfo} Consider profiling with browser DevTools.`;
}
if (probability >= 60) {
return `Warning: Possible memory leak detected. Memory trend is ${trend}.${gcInfo} Monitor closely and check for retained references.`;
}
if (probability >= 30 && trend === "increasing") {
return `Note: Memory usage is trending upward.${gcInfo} This may be normal for your application, but consider monitoring.`;
}
return undefined;
}
/**
* Format growth rate for display.
*
* @param bytesPerSample - Growth rate in bytes per sample
* @returns Formatted string
*/
function formatGrowthRate(bytesPerSample: number): string {
const absBytes = Math.abs(bytesPerSample);
Eif (absBytes >= 1024 * 1024) {
return `${(bytesPerSample / (1024 * 1024)).toFixed(2)} MB`;
}
if (absBytes >= 1024) {
return `${(bytesPerSample / 1024).toFixed(2)} KB`;
}
return `${bytesPerSample.toFixed(0)} bytes`;
}
/**
* Calculate leak probability using multiple weighted factors.
*
* Factors:
* - Slope contribution (0-30): How fast memory is growing
* - R² contribution (0-20): How consistent the growth pattern is
* - GC contribution (0-25): Whether GC fails to reclaim memory
* - Time contribution (0-15): Longer observation = more confidence
* - Baseline contribution (0-10): Whether post-GC baseline is rising
*/
function calculateWeightedProbability(
slope: number,
rSquared: number,
gcAnalysis: GCAnalysis,
baselineAnalysis: BaselineAnalysis,
observationTime: number,
config: typeof LEAK_SENSITIVITY_CONFIG["medium"]
): { probability: number; factors: LeakProbabilityFactors } {
const factors: LeakProbabilityFactors = {
slopeContribution: 0,
rSquaredContribution: 0,
gcContribution: 0,
timeContribution: 0,
baselineContribution: 0,
};
// 1. Slope contribution (max 30 points)
if (slope > 0) {
const slopeRatio = slope / config.minSlope;
factors.slopeContribution = Math.min(30, slopeRatio * 15);
}
// 2. R² contribution (max 20 points)
if (rSquared >= config.minR2) {
const rSquaredBonus = (rSquared - config.minR2) / (1 - config.minR2);
factors.rSquaredContribution = 10 + rSquaredBonus * 10;
}
// 3. GC contribution (max 25 points)
// If GC is happening but not reclaiming memory, it's likely a leak
if (gcAnalysis.gcEventCount >= config.minGCCycles) {
Iif (!gcAnalysis.isGCEffective) {
// GC is happening but not effective
factors.gcContribution = 25;
E} else if (gcAnalysis.avgRecoveryRatio < 0.3) {
// GC recovery is poor
factors.gcContribution = 15;
}
} else if (gcAnalysis.gcEventCount === 0 && slope > 0) {
// No GC detected but memory growing - could be early stage leak
factors.gcContribution = 5;
}
// 4. Time contribution (max 15 points)
Eif (observationTime >= config.minObservationTime) {
const timeRatio = Math.min(2, observationTime / config.minObservationTime);
factors.timeContribution = timeRatio * 7.5;
}
// 5. Baseline contribution (max 10 points)
if (baselineAnalysis.isBaselineEstablished && baselineAnalysis.isSignificantGrowth) {
factors.baselineContribution = 10;
} else if (baselineAnalysis.isSignificantGrowth) {
factors.baselineContribution = 5;
}
// Sum all contributions
const rawProbability =
factors.slopeContribution +
factors.rSquaredContribution +
factors.gcContribution +
factors.timeContribution +
factors.baselineContribution;
// Apply sensitivity multiplier and clamp
const probability = Math.min(100, Math.max(0, rawProbability * config.probabilityMultiplier));
return { probability, factors };
}
/**
* Analyze memory samples for potential leaks with enhanced algorithm.
*
* The enhanced algorithm considers:
* 1. GC cycles - true leaks persist even after GC
* 2. Baseline trend - post-GC memory should not grow over time
* 3. Observation time - requires sufficient data before making judgment
* 4. Multiple factors - weighted scoring system for accuracy
*
* @param samples - Array of memory info samples (minimum 10 recommended)
* @param sensitivity - Detection sensitivity level
* @param customThreshold - Optional custom growth threshold (bytes/sample)
* @returns Leak analysis result
*/
export function analyzeLeakProbability(
samples: MemoryInfo[],
sensitivity: LeakSensitivity = "medium",
customThreshold?: number
): LeakAnalysis {
const config = LEAK_SENSITIVITY_CONFIG[sensitivity];
const trend = calculateTrend(samples);
const averageGrowth = calculateAverageGrowth(samples);
// Not enough samples for reliable analysis
if (samples.length < MIN_LEAK_DETECTION_SAMPLES) {
return {
isLeaking: false,
probability: 0,
trend,
averageGrowth,
rSquared: 0,
samples,
recommendation: undefined,
confidence: 0,
};
}
// Calculate observation time
const observationTime = calculateObservationTime(samples);
// Not enough observation time
if (observationTime < config.minObservationTime) {
return {
isLeaking: false,
probability: 0,
trend,
averageGrowth,
rSquared: 0,
samples,
recommendation: undefined,
observationTime,
confidence: Math.round((observationTime / config.minObservationTime) * 50),
};
}
// Perform linear regression
const points: [number, number][] = samples.map((s, i) => [i, s.heapUsed]);
const { slope, rSquared } = linearRegression(points);
const threshold = customThreshold ?? config.minSlope;
// Detect GC events
const gcAnalysis = detectGCEvents(samples);
// Calculate baseline
const baselineAnalysis = calculateBaseline(samples, gcAnalysis.gcEvents);
// Check baseline trend if we have enough GC cycles
const baselineTrend = analyzeBaselineTrend(gcAnalysis.gcEvents);
// Calculate weighted probability
const { probability: rawProbability, factors } = calculateWeightedProbability(
slope,
rSquared,
gcAnalysis,
baselineAnalysis,
observationTime,
config
);
// Apply additional checks for false positive reduction
let probability = rawProbability;
// If GC is effective and baseline is stable, reduce probability
if (gcAnalysis.isGCEffective && baselineTrend.trend !== "increasing") {
probability = Math.max(0, probability - 20);
}
// If trend is decreasing or stable, cap probability
if (trend === "decreasing") {
probability = Math.min(probability, 10);
} else if (trend === "stable") {
probability = Math.min(probability, 30);
}
// If slope is negative or very small, cap probability
if (slope <= 0 || slope < threshold * 0.3) {
probability = Math.min(probability, 20);
}
// Round probability
probability = Math.round(probability);
// Calculate confidence based on data quality
const confidence = Math.round(
Math.min(100,
(samples.length / 20) * 25 + // Sample count contribution
(gcAnalysis.gcEventCount >= config.minGCCycles ? 25 : gcAnalysis.gcEventCount * 10) + // GC observation
(observationTime / config.minObservationTime) * 25 + // Time contribution
rSquared * 25 // Fit quality
)
);
// Determine if leaking (using stricter threshold)
const isLeaking = probability >= LEAK_PROBABILITY_THRESHOLD;
return {
isLeaking,
probability,
trend,
averageGrowth: slope,
rSquared,
samples,
recommendation: generateRecommendation(probability, trend, slope, gcAnalysis),
gcAnalysis,
baselineAnalysis,
observationTime,
confidence,
factors,
};
}
/**
* Quick check if memory is trending upward (without full analysis).
*
* @param samples - Array of memory info samples
* @returns True if memory appears to be growing
*/
export function isMemoryGrowing(samples: MemoryInfo[]): boolean {
if (samples.length < 3) {
return false;
}
const trend = calculateTrend(samples);
return trend === "increasing";
}
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