/performance-regression-debugging

name: performance-regression-debugging
description: >
  Identify and debug performance regressions from code changes. Use comparison
  and profiling to locate what degraded performance and restore baseline
  metrics.

Performance Regression Debugging

Table of Contents

• [Overview](#overview)
• [When to Use](#when-to-use)
• [Quick Start](#quick-start)
• [Reference Guides](#reference-guides)
• [Best Practices](#best-practices)

Overview

Performance regressions occur when code changes degrade application performance. Detection and quick resolution are critical.

When to Use

• After deployment performance degrades
• Metrics show negative trend
• User complaints about slowness
• A/B testing shows variance
• Regular performance monitoring

Quick Start

Minimal working example:

// Before: 500ms response time
// After: 1000ms response time (2x slower = regression)

// Capture baseline metrics
const baseline = {
  responseTime: 500, // ms
  timeToInteractive: 2000, // ms
  largestContentfulPaint: 1500, // ms
  memoryUsage: 50, // MB
  bundleSize: 150, // KB gzipped
};

// Monitor after change
const current = {
  responseTime: 1000,
  timeToInteractive: 4000,
  largestContentfulPaint: 3000,
  memoryUsage: 150,
  bundleSize: 200,
};

// Calculate regression
const regressions = {};
for (let metric in baseline) {
  const change = (current[metric] - baseline[metric]) / baseline[metric];
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the `references/` directory:

| Guide | Contents | |---|---| | [Detection & Measurement](references/detection-measurement.md) | Detection & Measurement | | [Root Cause Identification](references/root-cause-identification.md) | Root Cause Identification | | [Fixing & Verification](references/fixing-verification.md) | Fixing & Verification | | [Prevention Measures](references/prevention-measures.md) | Prevention Measures |

Best Practices

✅ DO

• Follow established patterns and conventions
• Write clean, maintainable code
• Add appropriate documentation
• Test thoroughly before deploying

❌ DON'T

• Skip testing or validation
• Ignore error handling
• Hard-code configuration values