@re-shell/cli
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Full-stack development platform uniting microservices and microfrontends. Build complete applications with .NET (ASP.NET Core Web API, Minimal API), Java (Spring Boot, Quarkus, Micronaut, Vert.x), Rust (Actix-Web, Warp, Rocket, Axum), Python (FastAPI, Dja
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JavaScript
"use strict";
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Object.defineProperty(o, "default", { enumerable: true, value: v });
}) : function(o, v) {
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});
var __importStar = (this && this.__importStar) || (function () {
var ownKeys = function(o) {
ownKeys = Object.getOwnPropertyNames || function (o) {
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if (mod && mod.__esModule) return mod;
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};
})();
Object.defineProperty(exports, "__esModule", { value: true });
exports.PerformanceProfiler = void 0;
exports.profilePerformance = profilePerformance;
exports.createBenchmark = createBenchmark;
const events_1 = require("events");
const fs = __importStar(require("fs-extra"));
const path = __importStar(require("path"));
const os = __importStar(require("os"));
const child_process_1 = require("child_process");
class PerformanceProfiler extends events_1.EventEmitter {
constructor(config = {}) {
super();
this.metrics = [];
this.optimizationToActionItem = (optimization) => ({
id: optimization.id,
title: optimization.title,
description: optimization.description,
priority: optimization.priority,
effort: optimization.implementation.timeEstimate,
timeline: optimization.implementation.timeEstimate,
owner: 'development-team',
dependencies: optimization.implementation.dependencies || [],
success: {
metrics: [optimization.type],
targets: optimization.metrics.after,
validation: optimization.implementation.steps
}
});
this.config = {
profileDuration: 60000,
sampleInterval: 1000,
includeMemoryProfile: true,
includeCpuProfile: true,
includeIOProfile: true,
includeNetworkProfile: false,
outputPath: './performance-reports',
generateReport: true,
optimization: {
analyzeBundleSize: true,
analyzeDependencies: true,
analyzeStartupTime: true,
analyzeMemoryUsage: true,
analyzeBuildTime: true,
generateRecommendations: true
},
thresholds: {
cpuUsage: 80,
memoryUsage: 500,
startupTime: 2000,
buildTime: 60000,
bundleSize: 10,
networkLatency: 1000
},
benchmarks: [],
...config
};
this.startTime = new Date();
this.endTime = new Date();
}
async profile(projectPath) {
this.emit('profile:start', { project: projectPath });
this.startTime = new Date();
try {
// Start metrics collection
await this.startMetricsCollection();
// Run benchmarks if configured
const benchmarks = await this.runBenchmarks(projectPath);
// Stop metrics collection
this.stopMetricsCollection();
this.endTime = new Date();
// Analyze performance data
const analysis = await this.analyzePerformance();
// Generate optimization recommendations
const optimizations = await this.generateOptimizations(projectPath);
// Create performance summary
const summary = this.generateSummary(analysis, optimizations);
const profile = {
config: this.config,
startTime: this.startTime,
endTime: this.endTime,
duration: this.endTime.getTime() - this.startTime.getTime(),
metrics: this.metrics,
benchmarks,
analysis,
optimizations,
summary
};
this.emit('profile:complete', profile);
return profile;
}
catch (error) {
this.emit('profile:error', error);
throw error;
}
}
async startMetricsCollection() {
this.emit('metrics:start');
this.intervalId = setInterval(async () => {
try {
const metrics = await this.collectMetrics();
this.metrics.push(metrics);
this.emit('metrics:collected', metrics);
}
catch (error) {
this.emit('metrics:error', error);
}
}, this.config.sampleInterval);
// Stop collection after configured duration
setTimeout(() => {
this.stopMetricsCollection();
}, this.config.profileDuration);
}
stopMetricsCollection() {
if (this.intervalId) {
clearInterval(this.intervalId);
this.intervalId = undefined;
}
this.emit('metrics:stop');
}
async collectMetrics() {
const timestamp = new Date();
const memUsage = process.memoryUsage();
const cpuUsage = process.cpuUsage();
return {
timestamp,
duration: timestamp.getTime() - this.startTime.getTime(),
cpu: await this.collectCpuMetrics(cpuUsage),
memory: this.collectMemoryMetrics(memUsage),
io: await this.collectIOMetrics(),
network: this.config.includeNetworkProfile ? await this.collectNetworkMetrics() : undefined,
process: this.collectProcessMetrics(),
system: this.collectSystemMetrics()
};
}
async collectCpuMetrics(cpuUsage) {
const loadAverage = os.loadavg();
const cpus = os.cpus();
return {
usage: this.calculateCpuUsage(cpuUsage),
userTime: cpuUsage.user / 1000,
systemTime: cpuUsage.system / 1000,
idleTime: 0, // Will be calculated based on sampling
loadAverage,
cores: cpus.length
};
}
calculateCpuUsage(cpuUsage) {
// Simple CPU usage calculation
const totalTime = cpuUsage.user + cpuUsage.system;
return Math.min(100, (totalTime / 1000000) * 100); // Convert to percentage
}
collectMemoryMetrics(memUsage) {
const totalMemory = os.totalmem();
const freeMemory = os.freemem();
return {
used: totalMemory - freeMemory,
total: totalMemory,
heap: {
used: memUsage.heapUsed,
total: memUsage.heapTotal,
limit: this.getHeapLimit(),
available: this.getHeapLimit() - memUsage.heapUsed
},
external: memUsage.external,
rss: memUsage.rss,
arrayBuffers: memUsage.arrayBuffers || 0
};
}
getHeapLimit() {
// Get V8 heap size limit
try {
const v8 = require('v8');
const heapStats = v8.getHeapStatistics();
return heapStats.heap_size_limit;
}
catch {
return 1024 * 1024 * 1024; // Default 1GB
}
}
async collectIOMetrics() {
// Mock I/O metrics - in real implementation, this would use system calls
return {
read: {
bytes: 0,
operations: 0,
time: 0,
throughput: 0
},
write: {
bytes: 0,
operations: 0,
time: 0,
throughput: 0
},
total: {
bytes: 0,
operations: 0,
time: 0,
efficiency: 100
}
};
}
async collectNetworkMetrics() {
// Mock network metrics - in real implementation, this would monitor network activity
return {
requests: 0,
totalBytes: 0,
averageLatency: 0,
errors: 0,
connections: 0
};
}
collectProcessMetrics() {
return {
pid: process.pid,
ppid: process.ppid,
startTime: new Date(Date.now() - process.uptime() * 1000),
uptime: process.uptime() * 1000,
threads: 1, // Node.js is single-threaded (main thread)
handles: 0 // Platform specific
};
}
collectSystemMetrics() {
return {
platform: os.platform(),
arch: os.arch(),
nodeVersion: process.version,
v8Version: process.versions.v8,
totalMemory: os.totalmem(),
freeMemory: os.freemem(),
uptime: os.uptime(),
loadAverage: os.loadavg()
};
}
async runBenchmarks(projectPath) {
this.emit('benchmarks:start');
const results = [];
for (const benchmark of this.config.benchmarks || []) {
try {
const result = await this.runBenchmark(benchmark, projectPath);
results.push(result);
this.emit('benchmark:complete', result);
}
catch (error) {
this.emit('benchmark:error', { benchmark, error });
}
}
this.emit('benchmarks:complete', results);
return results;
}
async runBenchmark(config, projectPath) {
const iterations = config.iterations || 5;
const warmupIterations = config.warmupIterations || 1;
const results = [];
// Warmup runs
for (let i = 0; i < warmupIterations; i++) {
await this.executeBenchmarkIteration(config, projectPath, i - warmupIterations);
}
// Actual benchmark runs
for (let i = 0; i < iterations; i++) {
const result = await this.executeBenchmarkIteration(config, projectPath, i);
results.push(result);
}
const statistics = this.calculateBenchmarkStatistics(results);
const baseline = await this.loadBenchmarkBaseline(config.name);
const regression = baseline ? this.analyzeRegression(statistics, baseline) : undefined;
return {
name: config.name,
command: config.command,
iterations,
results,
statistics,
baseline,
regression
};
}
async executeBenchmarkIteration(config, projectPath, iteration) {
const startTime = Date.now();
const memoryBefore = process.memoryUsage();
try {
const result = (0, child_process_1.execSync)(config.command, {
cwd: projectPath,
timeout: config.timeout || 30000,
env: { ...process.env, ...config.env },
stdio: 'pipe'
});
const endTime = Date.now();
const memoryAfter = process.memoryUsage();
return {
iteration,
duration: endTime - startTime,
exitCode: 0,
memory: {
timestamp: new Date(),
heap: {
used: memoryAfter.heapUsed,
total: memoryAfter.heapTotal,
limit: this.getHeapLimit(),
available: this.getHeapLimit() - memoryAfter.heapUsed
},
rss: memoryAfter.rss,
external: memoryAfter.external,
total: memoryAfter.rss + memoryAfter.external
},
cpu: 0, // Would need process.cpuUsage() tracking
success: true
};
}
catch (error) {
const endTime = Date.now();
return {
iteration,
duration: endTime - startTime,
exitCode: error.status || 1,
memory: {
timestamp: new Date(),
heap: {
used: memoryBefore.heapUsed,
total: memoryBefore.heapTotal,
limit: this.getHeapLimit(),
available: this.getHeapLimit() - memoryBefore.heapUsed
},
rss: memoryBefore.rss,
external: memoryBefore.external,
total: memoryBefore.rss + memoryBefore.external
},
cpu: 0,
success: false,
error: error.message
};
}
}
calculateBenchmarkStatistics(results) {
const durations = results.map(r => r.duration);
const successfulResults = results.filter(r => r.success);
durations.sort((a, b) => a - b);
const mean = durations.reduce((sum, val) => sum + val, 0) / durations.length;
const median = durations[Math.floor(durations.length / 2)];
const min = Math.min(...durations);
const max = Math.max(...durations);
const variance = durations.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / durations.length;
const stdDev = Math.sqrt(variance);
const percentiles = {};
[50, 75, 90, 95, 99].forEach(p => {
const index = Math.floor((p / 100) * durations.length);
percentiles[p] = durations[Math.min(index, durations.length - 1)];
});
return {
mean,
median,
min,
max,
stdDev,
variance,
percentiles,
successRate: (successfulResults.length / results.length) * 100,
throughput: successfulResults.length > 0 ? 1000 / mean : 0
};
}
async loadBenchmarkBaseline(benchmarkName) {
try {
const baselinePath = path.join(this.config.outputPath || '', 'baselines', `${benchmarkName}.json`);
if (await fs.pathExists(baselinePath)) {
return await fs.readJson(baselinePath);
}
}
catch (error) {
// Baseline not found or corrupted
}
return undefined;
}
analyzeRegression(statistics, baseline) {
const change = ((statistics.mean - baseline.mean) / baseline.mean) * 100;
const significant = Math.abs(change) > 5; // 5% threshold for significance
let trend;
if (change > 5) {
trend = 'degrading';
}
else if (change < -5) {
trend = 'improving';
}
else {
trend = 'stable';
}
return {
significant,
change,
confidence: significant ? 95 : 50,
trend,
pValue: significant ? 0.01 : 0.5
};
}
async analyzePerformance() {
this.emit('analysis:start');
const bottlenecks = this.identifyBottlenecks();
const patterns = this.identifyPatterns();
const trends = this.analyzeTrends();
const anomalies = this.detectAnomalies();
const efficiency = this.calculateEfficiency();
this.emit('analysis:complete');
return {
bottlenecks,
patterns,
trends,
anomalies,
efficiency
};
}
identifyBottlenecks() {
const bottlenecks = [];
if (this.metrics.length === 0)
return bottlenecks;
// Analyze CPU bottlenecks
const avgCpuUsage = this.metrics.reduce((sum, m) => sum + m.cpu.usage, 0) / this.metrics.length;
if (avgCpuUsage > this.config.thresholds.cpuUsage) {
bottlenecks.push({
type: 'cpu',
severity: avgCpuUsage > 95 ? 'critical' : avgCpuUsage > 85 ? 'high' : 'medium',
description: `High CPU usage detected (${avgCpuUsage.toFixed(1)}%)`,
impact: `System responsiveness may be affected`,
metrics: { avgCpuUsage },
recommendations: [
'Optimize CPU-intensive operations',
'Implement code profiling to identify hot spots',
'Consider worker threads for heavy computations'
]
});
}
// Analyze memory bottlenecks
const avgMemoryUsage = this.metrics.reduce((sum, m) => sum + m.memory.used, 0) / this.metrics.length;
const avgMemoryMB = avgMemoryUsage / (1024 * 1024);
if (avgMemoryMB > this.config.thresholds.memoryUsage) {
bottlenecks.push({
type: 'memory',
severity: avgMemoryMB > 1000 ? 'critical' : avgMemoryMB > 750 ? 'high' : 'medium',
description: `High memory usage detected (${avgMemoryMB.toFixed(1)}MB)`,
impact: `Risk of out-of-memory errors and performance degradation`,
metrics: { avgMemoryMB },
recommendations: [
'Implement memory profiling to identify leaks',
'Optimize data structures and algorithms',
'Use streaming for large data processing',
'Implement garbage collection optimization'
]
});
}
// Analyze heap bottlenecks
const avgHeapUsage = this.metrics.reduce((sum, m) => sum + (m.memory.heap.used / m.memory.heap.total), 0) / this.metrics.length * 100;
if (avgHeapUsage > 80) {
bottlenecks.push({
type: 'memory',
severity: avgHeapUsage > 95 ? 'critical' : avgHeapUsage > 90 ? 'high' : 'medium',
description: `High heap usage detected (${avgHeapUsage.toFixed(1)}%)`,
impact: `Frequent garbage collection may impact performance`,
metrics: { avgHeapUsage },
recommendations: [
'Reduce object allocations in hot paths',
'Implement object pooling for frequently created objects',
'Optimize data structures to reduce memory footprint'
]
});
}
return bottlenecks;
}
identifyPatterns() {
// Pattern analysis would be more sophisticated in a real implementation
const patterns = [];
if (this.metrics.length < 10)
return patterns;
// Memory growth pattern
const memoryGrowth = this.analyzeMemoryGrowth();
if (memoryGrowth.significant) {
patterns.push({
name: 'Memory Growth',
frequency: memoryGrowth.occurrences,
impact: memoryGrowth.rate > 1 ? 'negative' : 'neutral',
description: `Memory usage is ${memoryGrowth.rate > 0 ? 'increasing' : 'decreasing'} over time`,
occurrences: [{
timestamp: new Date(),
duration: this.metrics.length * this.config.sampleInterval,
context: 'Overall profiling period',
metrics: { growthRate: memoryGrowth.rate }
}]
});
}
return patterns;
}
analyzeMemoryGrowth() {
if (this.metrics.length < 2)
return { significant: false, rate: 0, occurrences: 0 };
const firstMemory = this.metrics[0].memory.used;
const lastMemory = this.metrics[this.metrics.length - 1].memory.used;
const duration = this.metrics[this.metrics.length - 1].timestamp.getTime() - this.metrics[0].timestamp.getTime();
const rate = ((lastMemory - firstMemory) / firstMemory) * 100;
const significant = Math.abs(rate) > 10; // 10% change threshold
return {
significant,
rate,
occurrences: significant ? 1 : 0
};
}
analyzeTrends() {
const trends = [];
if (this.metrics.length < 5)
return trends;
// CPU trend
const cpuTrend = this.calculateTrend(this.metrics.map(m => m.cpu.usage));
trends.push({
metric: 'CPU Usage',
direction: cpuTrend.direction,
rate: cpuTrend.rate,
confidence: cpuTrend.confidence,
significance: cpuTrend.significant
});
// Memory trend
const memoryValues = this.metrics.map(m => m.memory.used / (1024 * 1024));
const memoryTrend = this.calculateTrend(memoryValues);
trends.push({
metric: 'Memory Usage',
direction: memoryTrend.direction,
rate: memoryTrend.rate,
confidence: memoryTrend.confidence,
significance: memoryTrend.significant
});
return trends;
}
calculateTrend(values) {
if (values.length < 3) {
return { direction: 'stable', rate: 0, confidence: 0, significant: false };
}
// Simple linear regression
const n = values.length;
const x = Array.from({ length: n }, (_, i) => i);
const sumX = x.reduce((a, b) => a + b, 0);
const sumY = values.reduce((a, b) => a + b, 0);
const sumXY = x.reduce((sum, xi, i) => sum + xi * values[i], 0);
const sumXX = x.reduce((sum, xi) => sum + xi * xi, 0);
const slope = (n * sumXY - sumX * sumY) / (n * sumXX - sumX * sumX);
let direction;
if (Math.abs(slope) < 0.1) {
direction = 'stable';
}
else if (slope > 0) {
direction = 'increasing';
}
else {
direction = 'decreasing';
}
return {
direction,
rate: Math.abs(slope),
confidence: Math.min(95, Math.abs(slope) * 100),
significant: Math.abs(slope) > 0.5
};
}
detectAnomalies() {
const anomalies = [];
if (this.metrics.length < 10)
return anomalies;
// Detect CPU spikes
const cpuValues = this.metrics.map(m => m.cpu.usage);
const cpuMean = cpuValues.reduce((a, b) => a + b, 0) / cpuValues.length;
const cpuStdDev = Math.sqrt(cpuValues.reduce((sum, val) => sum + Math.pow(val - cpuMean, 2), 0) / cpuValues.length);
this.metrics.forEach((metric, index) => {
const deviation = Math.abs(metric.cpu.usage - cpuMean) / cpuStdDev;
if (deviation > 2) { // 2 standard deviations
anomalies.push({
timestamp: metric.timestamp,
type: metric.cpu.usage > cpuMean ? 'spike' : 'drop',
metric: 'CPU Usage',
value: metric.cpu.usage,
expectedValue: cpuMean,
deviation,
severity: deviation > 3 ? 'critical' : deviation > 2.5 ? 'high' : 'medium',
description: `CPU usage ${metric.cpu.usage > cpuMean ? 'spike' : 'drop'} detected`,
possibleCauses: [
'Intensive computation',
'Background process interference',
'Resource contention',
'System load'
]
});
}
});
return anomalies;
}
calculateEfficiency() {
if (this.metrics.length === 0) {
return {
overall: 0,
cpu: 0,
memory: 0,
io: 0,
improvement: 0
};
}
// Calculate CPU efficiency (lower usage for same output = higher efficiency)
const avgCpuUsage = this.metrics.reduce((sum, m) => sum + m.cpu.usage, 0) / this.metrics.length;
const cpuEfficiency = Math.max(0, 100 - avgCpuUsage);
// Calculate memory efficiency (lower usage = higher efficiency)
const avgMemoryUsage = this.metrics.reduce((sum, m) => sum + (m.memory.heap.used / m.memory.heap.total * 100), 0) / this.metrics.length;
const memoryEfficiency = Math.max(0, 100 - avgMemoryUsage);
// I/O efficiency (mock calculation)
const ioEfficiency = 85; // Mock value
const overall = (cpuEfficiency + memoryEfficiency + ioEfficiency) / 3;
const improvement = Math.max(0, 100 - overall);
return {
overall,
cpu: cpuEfficiency,
memory: memoryEfficiency,
io: ioEfficiency,
improvement
};
}
async generateOptimizations(projectPath) {
this.emit('optimization:start');
const optimizations = [];
if (this.config.optimization?.analyzeStartupTime) {
optimizations.push(...await this.analyzeStartupOptimizations(projectPath));
}
if (this.config.optimization?.analyzeMemoryUsage) {
optimizations.push(...await this.analyzeMemoryOptimizations());
}
if (this.config.optimization?.analyzeBundleSize) {
optimizations.push(...await this.analyzeBundleOptimizations(projectPath));
}
if (this.config.optimization?.analyzeDependencies) {
optimizations.push(...await this.analyzeDependencyOptimizations(projectPath));
}
if (this.config.optimization?.analyzeBuildTime) {
optimizations.push(...await this.analyzeBuildOptimizations(projectPath));
}
this.emit('optimization:complete', optimizations);
return optimizations;
}
async analyzeStartupOptimizations(projectPath) {
const optimizations = [];
// Startup time optimization
optimizations.push({
id: 'startup-lazy-loading',
type: 'startup',
priority: 'high',
title: 'Implement Lazy Loading for Heavy Dependencies',
description: 'Defer loading of heavy dependencies until they are actually needed to improve startup time',
impact: {
performance: 30,
memory: 20,
size: 0,
speed: 35
},
effort: 'medium',
implementation: {
steps: [
'Identify heavy dependencies using bundle analysis',
'Implement dynamic imports for non-critical modules',
'Use lazy loading patterns for command-specific dependencies',
'Add loading states for better user experience'
],
code: `
// Before
import heavyLibrary from 'heavy-library';
// After
const loadHeavyLibrary = () => import('heavy-library');
`,
automatizable: true,
timeEstimate: '2-4 hours'
},
benefits: [
'Faster startup time',
'Reduced memory usage on startup',
'Better user experience',
'Lower resource consumption'
],
risks: [
'Slight delay when feature is first used',
'Complexity in error handling',
'Potential for loading state management issues'
],
metrics: {
before: { startupTime: 2000 },
after: { startupTime: 1400 },
improvement: { startupTime: 30 }
}
});
// Code splitting optimization
optimizations.push({
id: 'startup-code-splitting',
type: 'startup',
priority: 'medium',
title: 'Implement Command-Based Code Splitting',
description: 'Split code by commands to load only necessary code for each operation',
impact: {
performance: 25,
memory: 15,
size: 10,
speed: 30
},
effort: 'high',
implementation: {
steps: [
'Analyze command usage patterns',
'Split commands into separate modules',
'Implement dynamic command loading',
'Update CLI router to handle dynamic loading'
],
config: {
'webpack.config.js': {
optimization: {
splitChunks: {
chunks: 'all',
cacheGroups: {
commands: {
test: /src\/commands/,
name: 'commands',
chunks: 'all'
}
}
}
}
}
},
automatizable: false,
timeEstimate: '1-2 days'
},
benefits: [
'Faster startup for specific commands',
'Reduced bundle size per command',
'Better caching strategies',
'Improved maintainability'
],
risks: [
'Increased complexity',
'Potential for loading errors',
'Build process complications'
],
metrics: {
before: { bundleSize: 10 },
after: { bundleSize: 8 },
improvement: { bundleSize: 20 }
}
});
return optimizations;
}
async analyzeMemoryOptimizations() {
const optimizations = [];
if (this.metrics.length === 0)
return optimizations;
const avgHeapUsage = this.metrics.reduce((sum, m) => sum + (m.memory.heap.used / m.memory.heap.total), 0) / this.metrics.length;
if (avgHeapUsage > 0.7) {
optimizations.push({
id: 'memory-object-pooling',
type: 'memory',
priority: 'high',
title: 'Implement Object Pooling for Frequently Created Objects',
description: 'Reduce garbage collection pressure by reusing objects instead of creating new ones',
impact: {
performance: 20,
memory: 40,
size: 0,
speed: 15
},
effort: 'medium',
implementation: {
steps: [
'Identify frequently allocated objects',
'Create object pools for these objects',
'Implement pool management (acquire/release)',
'Monitor pool effectiveness'
],
code: `
class ObjectPool<T> {
private pool: T[] = [];
private createFn: () => T;
constructor(createFn: () => T, initialSize = 10) {
this.createFn = createFn;
for (let i = 0; i < initialSize; i++) {
this.pool.push(createFn());
}
}
acquire(): T {
return this.pool.pop() || this.createFn();
}
release(obj: T): void {
this.pool.push(obj);
}
}
`,
automatizable: false,
timeEstimate: '4-6 hours'
},
benefits: [
'Reduced garbage collection',
'Lower memory allocation rate',
'Improved performance consistency',
'Better memory utilization'
],
risks: [
'Complexity in object lifecycle management',
'Potential memory leaks if not handled properly',
'Debugging complexity'
],
metrics: {
before: { heapUsage: avgHeapUsage * 100 },
after: { heapUsage: (avgHeapUsage * 0.7) * 100 },
improvement: { heapUsage: 30 }
}
});
}
return optimizations;
}
async analyzeBundleOptimizations(projectPath) {
const optimizations = [];
try {
// Check if there's a package.json to analyze
const packageJsonPath = path.join(projectPath, 'package.json');
if (await fs.pathExists(packageJsonPath)) {
const packageJson = await fs.readJson(packageJsonPath);
// Check for heavy dependencies
const deps = { ...packageJson.dependencies, ...packageJson.devDependencies };
const heavyDeps = Object.keys(deps).filter(dep => ['webpack', 'typescript', 'eslint', 'babel'].some(heavy => dep.includes(heavy)));
if (heavyDeps.length > 0) {
optimizations.push({
id: 'bundle-tree-shaking',
type: 'bundle',
priority: 'medium',
title: 'Enable Tree Shaking for Unused Code Elimination',
description: 'Remove unused code from dependencies to reduce bundle size',
impact: {
performance: 15,
memory: 10,
size: 30,
speed: 20
},
effort: 'low',
implementation: {
steps: [
'Enable ES modules in package.json',
'Configure bundler for tree shaking',
'Mark side-effect-free modules',
'Analyze bundle to verify improvements'
],
config: {
'package.json': {
type: 'module',
sideEffects: false
}
},
automatizable: true,
timeEstimate: '1-2 hours'
},
benefits: [
'Smaller bundle size',
'Faster loading times',
'Reduced memory usage',
'Better performance'
],
risks: [
'Potential for breaking changes',
'Build configuration complexity'
],
metrics: {
before: { bundleSize: 10 },
after: { bundleSize: 7 },
improvement: { bundleSize: 30 }
}
});
}
}
}
catch (error) {
// Ignore errors in bundle analysis
}
return optimizations;
}
async analyzeDependencyOptimizations(projectPath) {
const optimizations = [];
try {
const packageJsonPath = path.join(projectPath, 'package.json');
if (await fs.pathExists(packageJsonPath)) {
const packageJson = await fs.readJson(packageJsonPath);
const deps = packageJson.dependencies || {};
// Look for optimization opportunities
if (Object.keys(deps).length > 20) {
optimizations.push({
id: 'dependency-audit',
type: 'dependency',
priority: 'medium',
title: 'Audit and Optimize Dependencies',
description: 'Review and optimize project dependencies to reduce bundle size and improve performance',
impact: {
performance: 25,
memory: 20,
size: 35,
speed: 30
},
effort: 'medium',
implementation: {
steps: [
'Run dependency analyzer to identify unused dependencies',
'Remove unused dependencies',
'Replace heavy dependencies with lighter alternatives',
'Bundle analyze to verify improvements'
],
dependencies: ['webpack-bundle-analyzer', 'depcheck'],
automatizable: true,
timeEstimate: '2-4 hours'
},
benefits: [
'Reduced bundle size',
'Faster installation',
'Lower security attack surface',
'Improved maintainability'
],
risks: [
'Potential for breaking changes',
'Need for thorough testing'
],
metrics: {
before: { dependencies: Object.keys(deps).length },
after: { dependencies: Math.floor(Object.keys(deps).length * 0.8) },
improvement: { dependencies: 20 }
}
});
}
}
}
catch (error) {
// Ignore errors in dependency analysis
}
return optimizations;
}
async analyzeBuildOptimizations(projectPath) {
const optimizations = [];
// Build time optimization
optimizations.push({
id: 'build-incremental',
type: 'build',
priority: 'high',
title: 'Implement Incremental Build System',
description: 'Use incremental builds to only rebuild changed files and their dependencies',
impact: {
performance: 50,
memory: 10,
size: 0,
speed: 60
},
effort: 'high',
implementation: {
steps: [
'Set up build cache system',
'Implement file change detection',
'Configure incremental TypeScript compilation',
'Add cache invalidation strategies'
],
config: {
'tsconfig.json': {
compilerOptions: {
incremental: true,
tsBuildInfoFile: '.tsbuildinfo'
}
}
},
automatizable: true,
timeEstimate: '4-8 hours'
},
benefits: [
'Dramatically faster builds',
'Improved developer experience',
'Reduced CI/CD time',
'Lower resource usage'
],
risks: [
'Cache invalidation complexity',
'Initial setup complexity',
'Storage requirements for cache'
],
metrics: {
before: { buildTime: 60000 },
after: { buildTime: 15000 },
improvement: { buildTime: 75 }
}
});
return optimizations;
}
generateSummary(analysis, optimizations) {
const overallScore = this.calculateOverallScore(analysis);
const categories = {
cpu: this.calculateCategoryScore('cpu', analysis),
memory: this.calculateCategoryScore('memory', analysis),
io: this.calculateCategoryScore('io', analysis),
startup: this.calculateCategoryScore('startup', analysis)
};
const improvements = this.generateImprovements(optimizations);
const warnings = this.generateWarnings(analysis);
const achievements = this.generateAchievements(analysis);
return {
overall: overallScore,
categories,
improvements,
warnings,
achievements
};
}
calculateOverallScore(analysis) {
const score = analysis.efficiency.overall;
return {
score,
grade: this.scoreToGrade(score),
trend: 'stable', // Would be calculated from historical data
change: 0 // Would be calculated from previous assessments
};
}
calculateCategoryScore(category, analysis) {
let score = 100;
// Reduce score based on bottlenecks in this category
const categoryBottlenecks = analysis.bottlenecks.filter(b => category === 'startup' ? b.type === 'startup' : b.type === category);
for (const bottleneck of categoryBottlenecks) {
switch (bottleneck.severity) {
case 'critical':
score -= 30;
break;
case 'high':
score -= 20;
break;
case 'medium':
score -= 10;
break;
case 'low':
score -= 5;
break;
}
}
score = Math.max(0, score);
return {
score,
grade: this.scoreToGrade(score),
trend: 'stable',
change: 0
};
}
scoreToGrade(score) {
if (score >= 90)
return 'A';
if (score >= 80)
return 'B';
if (score >= 70)
return 'C';
if (score >= 60)
return 'D';
return 'F';
}
generateImprovements(optimizations) {
return optimizations.slice(0, 5).map(opt => ({
area: opt.type,
potential: opt.impact.performance,
effort: opt.effort,
impact: opt.priority === 'critical' || opt.priority === 'high' ? 'high' :
opt.priority === 'medium' ? 'medium' : 'low'
}));
}
generateWarnings(analysis) {
const warnings = [];
for (const bottleneck of analysis.bottlenecks) {
if (bottleneck.severity === 'critical' || bottleneck.severity === 'high') {
warnings.push({
severity: bottleneck.severity,
message: bottleneck.description,
metric: bottleneck.type,
threshold: this.getThresholdForType(bottleneck.type),
current: Object.values(bottleneck.metrics)[0] || 0
});
}
}
return warnings;
}
getThresholdForType(type) {
switch (type) {
case 'cpu': return this.config.thresholds.cpuUsage;
case 'memory': return this.config.thresholds.memoryUsage;
case 'startup': return this.config.thresholds.startupTime;
case 'build': return this.config.thresholds.buildTime;
default: return 100;
}
}
generateAchievements(analysis) {
const achievements = [];
if (analysis.efficiency.overall > 80) {
achievements.push({
title: 'High Efficiency',
description: 'System is running with high efficiency',
metric: 'Overall Efficiency',
value: analysis.efficiency.overall
});
}
if (analysis.anomalies.length === 0) {
achievements.push({
title: 'Stable Performance',
description: 'No performance anomalies detected',
metric: 'Anomalies',
value: 0
});
}
return achievements;
}
async generateReport(profile) {
const executive = this.generateExecutiveSummary(profile);
const technical = this.generateTechnicalDetails(profile);
const actionPlan = this.generateActionPlan(profile);
const appendices = this.generateAppendices(profile);
const report = {
profile,
executive,
technical,
actionPlan,
appendices,
timestamp: new Date()
};
if (this.config.generateReport) {
await this.saveReport(report);
}
return report;
}
generateExecutiveSummary(profile) {
const criticalIssues = profile.analysis.bottlenecks.filter(b => b.severity === 'critical').length;
return {
overallScore: profile.summary.overall.score,
keyFindings: [
`Overall performance score: ${profile.summary.overall.score}/100`,
`${profile.analysis.bottlenecks.length} performance bottlenecks identified`,
`${profile.optimizations.length} optimization opportunities found`
],
criticalIssues,
potentialSavings: {
timeReduction: profile.optimizations.reduce((sum, opt) => sum + (opt.impact.speed || 0), 0),
memoryReduction: profile.optimizations.reduce((sum, opt) => sum + (opt.impact.memory || 0), 0),
costSavings: 0, // Would be calculated based on resource costs
efficiencyGain: profile.optimizations.reduce((sum, opt) => sum + (opt.impact.performance || 0), 0) / profile.optimizations.length
},
recommendations: profile.optimizations.slice(0, 3).map(opt => ({
priority: opt.priority,
category: opt.type,
impact: `${opt.impact.performance}% performance improvement`,
effort: opt.effort,
timeline: opt.implementation.timeEstimate,
roi: opt.impact.performance / (opt.effort === 'low' ? 1 : opt.effort === 'medium' ? 2 : 3)
}))
};
}
generateTechnicalDetails(profile) {
return {
methodology: 'Performance profiling using Node.js process metrics and custom benchmarking',
environment: {
platform: os.platform(),
nodeVersion: process.version,
cpuInfo: os.cpus(),
memory: os.totalmem(),
timestamp: profile.startTime
},
dataCollection: {
duration: profile.duration,
sampleCount: profile.metrics.length,
sampleRate: this.config.sampleInterval || 1000,
metrics: ['CPU', 'Memory', 'I/O', 'Process', 'System']
},
analysis: {
algorithms: ['Linear regression', 'Statistical analysis', 'Anomaly detection'],
thresholds: this.config.thresholds,
statisticalMethods: ['Standard deviation', 'Percentile analysis', 'Trend analysis'],
confidence: 95
},
limitations: [
'I/O metrics are simulated in this implementation',
'Network metrics are optional and may not be available',
'Historical trend analysis requires multiple profiling sessions'
]
};
}
generateActionPlan(profile) {
const critical = profile.optimizations.filter(opt => opt.priority === 'critical');
const high = profile.optimizations.filter(opt => opt.priority === 'high');
const medium = profile.optimizations.filter(opt => opt.priority === 'medium');
return {
immediate: critical.map(this.optimizationToActionItem),
shortTerm: high.map(this.optimizationToActionItem),
longTerm: medium.map(this.optimizationToActionItem),
monitoring: {
frequency: 'weekly',
metrics: ['CPU usage', 'Memory usage', 'Startup time', 'Build time'],
alerts: [
{
metric: 'CPU usage',
threshold: this.config.thresholds.cpuUsage,
severity: 'high',
action: 'Investigate high CPU usage'
},
{
metric: 'Memory usage',
threshold: this.config.thresholds.memoryUsage,
severity: 'high',
action: 'Investigate memory leaks'
}
],
reporting: {
frequency: 'weekly',
format: 'summary',
recipients: ['development-team']
}
}
};
}
generateAppendices(profile) {
return {
rawData: profile.metrics,
benchmarkData: profile.benchmarks,
statisticalAnalysis: {
sampleSize: profile.metrics.length,
confidence: 95,
margin: 5,
significance: 0.05,
correlations: {} // Would be calculated from actual data
},
methodologyDetails: 'Detailed methodology and analysis techniques used for performance profiling'
};
}
async saveReport(