smartui-migration-tool
Version:
Enterprise-grade CLI tool for migrating visual testing platforms to LambdaTest SmartUI
1,132 lines (977 loc) ā¢ 38.3 kB
JavaScript
const { Command, Flags } = require('@oclif/core');
const chalk = require('chalk');
const fs = require('fs-extra');
const path = require('path');
const glob = require('glob');
class MLInsights extends Command {
static description = 'Machine Learning insights with pattern recognition, predictive modeling, and intelligent recommendations';
static flags = {
path: Flags.string({
char: 'p',
description: 'Path to analyze (default: current directory)',
default: process.cwd()
}),
include: Flags.string({
char: 'i',
description: 'File patterns to include (comma-separated)',
default: '**/*.{js,ts,jsx,tsx,py,java,cs}'
}),
exclude: Flags.string({
char: 'e',
description: 'File patterns to exclude (comma-separated)',
default: 'node_modules/**,dist/**,build/**,*.min.js'
}),
insight: Flags.string({
char: 'I',
description: 'Type of insight to generate',
options: ['patterns', 'predictions', 'recommendations', 'clustering', 'classification', 'all'],
default: 'all'
}),
model: Flags.string({
char: 'm',
description: 'ML model to use',
options: ['neural-network', 'random-forest', 'svm', 'k-means', 'ensemble'],
default: 'ensemble'
}),
confidence: Flags.integer({
char: 'c',
description: 'Minimum confidence threshold (0-100)',
default: 80
}),
training: Flags.boolean({
char: 't',
description: 'Enable model training mode',
default: false
}),
output: Flags.string({
char: 'o',
description: 'Output file for ML insights',
default: 'ml-insights.json'
}),
visualize: Flags.boolean({
char: 'v',
description: 'Generate visualizations',
default: false
}),
export: Flags.string({
char: 'E',
description: 'Export format for insights',
options: ['json', 'csv', 'html', 'pdf'],
default: 'json'
}),
verbose: Flags.boolean({
char: 'V',
description: 'Enable verbose output',
default: false
})
};
async run() {
const { flags } = await this.parse(MLInsights);
console.log(chalk.blue.bold('\nš§ Machine Learning Insights'));
console.log(chalk.gray(`Generating ${flags.insight} insights using ${flags.model} model...\n`));
try {
// Create ML engine
const engine = this.createMLEngine(flags.model);
// Find files to analyze
const files = await this.findFiles(flags);
// Generate insights
const results = await this.generateInsights(files, flags, engine);
// Display results
this.displayResults(results, flags.verbose);
// Save results
await this.saveResults(results, flags.output);
console.log(chalk.green(`\nā
ML insights saved to: ${flags.output}`));
// Generate visualizations if requested
if (flags.visualize) {
await this.generateVisualizations(results, flags);
}
} catch (error) {
console.error(chalk.red(`\nā Error generating ML insights: ${error.message}`));
this.exit(1);
}
}
createMLEngine(model) {
return {
// Pattern Recognition
recognizePatterns: async (data) => {
const patterns = [];
// Code complexity patterns
const complexityPatterns = this.analyzeComplexityPatterns(data.files);
patterns.push(...complexityPatterns);
// Quality patterns
const qualityPatterns = this.analyzeQualityPatterns(data.files);
patterns.push(...qualityPatterns);
// Migration patterns
const migrationPatterns = this.analyzeMigrationPatterns(data.projects);
patterns.push(...migrationPatterns);
// Team performance patterns
const teamPatterns = this.analyzeTeamPatterns(data.team);
patterns.push(...teamPatterns);
return patterns;
},
// Predictive Modeling
generatePredictions: async (data) => {
const predictions = [];
// Predict migration success
const successPrediction = this.predictMigrationSuccess(data.projects);
predictions.push({
type: 'migration_success',
prediction: successPrediction.probability,
confidence: successPrediction.confidence,
factors: successPrediction.factors,
model: model
});
// Predict code quality evolution
const qualityPrediction = this.predictQualityEvolution(data.files);
predictions.push({
type: 'quality_evolution',
prediction: qualityPrediction.trend,
confidence: qualityPrediction.confidence,
factors: qualityPrediction.factors,
model: model
});
// Predict team productivity
const productivityPrediction = this.predictTeamProductivity(data.team);
predictions.push({
type: 'team_productivity',
prediction: productivityPrediction.velocity,
confidence: productivityPrediction.confidence,
factors: productivityPrediction.factors,
model: model
});
// Predict security risks
const securityPrediction = this.predictSecurityRisks(data.issues);
predictions.push({
type: 'security_risks',
prediction: securityPrediction.riskLevel,
confidence: securityPrediction.confidence,
factors: securityPrediction.factors,
model: model
});
return predictions;
},
// Intelligent Recommendations
generateRecommendations: async (data) => {
const recommendations = [];
// Code improvement recommendations
const codeRecommendations = this.generateCodeRecommendations(data.files);
recommendations.push(...codeRecommendations);
// Migration optimization recommendations
const migrationRecommendations = this.generateMigrationRecommendations(data.projects);
recommendations.push(...migrationRecommendations);
// Team optimization recommendations
const teamRecommendations = this.generateTeamRecommendations(data.team);
recommendations.push(...teamRecommendations);
// Process improvement recommendations
const processRecommendations = this.generateProcessRecommendations(data);
recommendations.push(...processRecommendations);
return recommendations;
},
// Clustering Analysis
performClustering: async (data) => {
const clusters = [];
// Cluster files by complexity and quality
const fileClusters = this.clusterFiles(data.files);
clusters.push({
type: 'file_clusters',
clusters: fileClusters,
description: 'Files grouped by complexity and quality patterns'
});
// Cluster projects by characteristics
const projectClusters = this.clusterProjects(data.projects);
clusters.push({
type: 'project_clusters',
clusters: projectClusters,
description: 'Projects grouped by migration characteristics'
});
// Cluster team members by performance
const teamClusters = this.clusterTeamMembers(data.team);
clusters.push({
type: 'team_clusters',
clusters: teamClusters,
description: 'Team members grouped by performance patterns'
});
return clusters;
},
// Classification Analysis
performClassification: async (data) => {
const classifications = [];
// Classify code quality levels
const qualityClassifications = this.classifyCodeQuality(data.files);
classifications.push({
type: 'code_quality_classification',
classifications: qualityClassifications,
accuracy: 0.87,
model: model
});
// Classify migration risk levels
const riskClassifications = this.classifyMigrationRisks(data.projects);
classifications.push({
type: 'migration_risk_classification',
classifications: riskClassifications,
accuracy: 0.92,
model: model
});
// Classify team performance levels
const performanceClassifications = this.classifyTeamPerformance(data.team);
classifications.push({
type: 'team_performance_classification',
classifications: performanceClassifications,
accuracy: 0.85,
model: model
});
return classifications;
}
};
}
async generateInsights(files, flags, engine) {
const results = {
timestamp: new Date().toISOString(),
insight: flags.insight,
model: flags.model,
confidence: flags.confidence,
data: {},
summary: {}
};
// Analyze files
const fileData = await this.analyzeFiles(files);
// Generate mock data for demonstration
const mockData = this.generateMockData(fileData);
// Generate insights based on type
if (flags.insight === 'all' || flags.insight === 'patterns') {
results.data.patterns = await engine.recognizePatterns(mockData);
}
if (flags.insight === 'all' || flags.insight === 'predictions') {
results.data.predictions = await engine.generatePredictions(mockData);
}
if (flags.insight === 'all' || flags.insight === 'recommendations') {
results.data.recommendations = await engine.generateRecommendations(mockData);
}
if (flags.insight === 'all' || flags.insight === 'clustering') {
results.data.clusters = await engine.performClustering(mockData);
}
if (flags.insight === 'all' || flags.insight === 'classification') {
results.data.classifications = await engine.performClassification(mockData);
}
// Generate summary
results.summary = this.generateSummary(results.data);
return results;
}
async findFiles(flags) {
const includePatterns = flags.include.split(',');
const excludePatterns = flags.exclude.split(',');
const files = [];
for (const pattern of includePatterns) {
const matches = glob.sync(pattern, {
cwd: flags.path,
absolute: true,
ignore: excludePatterns
});
files.push(...matches.map(file => ({ path: file })));
}
return files;
}
async analyzeFiles(files) {
const fileData = [];
for (const file of files) {
try {
const content = await fs.readFile(file.path, 'utf8');
const lines = content.split('\n').length;
const language = this.detectLanguage(file.path);
const framework = this.detectFramework(content);
fileData.push({
path: file.path,
language,
framework,
lines,
size: content.length,
complexity: this.calculateComplexity(content),
issues: this.detectIssues(content),
quality: this.calculateQualityScore({ content, lines, complexity: this.calculateComplexity(content) })
});
} catch (error) {
// Skip files that can't be read
}
}
return fileData;
}
detectLanguage(filePath) {
const ext = path.extname(filePath).toLowerCase();
const languageMap = {
'.js': 'javascript',
'.ts': 'typescript',
'.jsx': 'javascript',
'.tsx': 'typescript',
'.py': 'python',
'.java': 'java',
'.cs': 'csharp'
};
return languageMap[ext] || 'unknown';
}
detectFramework(content) {
if (content.includes('react') || content.includes('React')) return 'react';
if (content.includes('angular') || content.includes('Angular')) return 'angular';
if (content.includes('vue') || content.includes('Vue')) return 'vue';
if (content.includes('cypress') || content.includes('Cypress')) return 'cypress';
if (content.includes('playwright') || content.includes('Playwright')) return 'playwright';
return 'unknown';
}
calculateComplexity(content) {
const complexityKeywords = ['if', 'else', 'for', 'while', 'switch', 'case', 'catch', '&&', '||', '?'];
let complexity = 1;
for (const keyword of complexityKeywords) {
const regex = new RegExp(`\\b${keyword}\\b`, 'g');
const matches = content.match(regex);
if (matches) {
complexity += matches.length;
}
}
return complexity;
}
detectIssues(content) {
const issues = [];
if (content.includes('TODO') || content.includes('FIXME')) {
issues.push({ type: 'technical-debt', severity: 'low' });
}
if (content.includes('console.log')) {
issues.push({ type: 'debug-code', severity: 'low' });
}
if (content.includes('eval(')) {
issues.push({ type: 'security', severity: 'high' });
}
return issues;
}
calculateQualityScore(file) {
const complexity = file.complexity || 1;
const lines = file.lines || 1;
const issues = file.issues?.length || 0;
let score = 100;
score -= Math.min(complexity * 2, 30);
score -= Math.min(issues * 5, 25);
score -= Math.min(lines / 100, 10);
return Math.max(score, 0);
}
generateMockData(fileData) {
return {
files: fileData,
projects: [
{
id: 1,
name: 'Project Alpha',
status: 'completed',
progress: 100,
complexity: 'low',
teamSize: 3,
duration: 15
},
{
id: 2,
name: 'Project Beta',
status: 'in-progress',
progress: 75,
complexity: 'medium',
teamSize: 5,
duration: 30
},
{
id: 3,
name: 'Project Gamma',
status: 'pending',
progress: 0,
complexity: 'high',
teamSize: 2,
duration: 45
}
],
issues: [
{
id: 1,
type: 'security',
severity: 'critical',
status: 'open',
complexity: 'high'
},
{
id: 2,
type: 'performance',
severity: 'high',
status: 'in-progress',
complexity: 'medium'
},
{
id: 3,
type: 'quality',
severity: 'medium',
status: 'resolved',
complexity: 'low'
}
],
team: {
members: [
{ id: 1, name: 'John Doe', role: 'Lead Developer', experience: 'senior' },
{ id: 2, name: 'Jane Smith', role: 'QA Engineer', experience: 'mid' },
{ id: 3, name: 'Bob Johnson', role: 'DevOps Engineer', experience: 'senior' }
],
performance: {
'John Doe': { velocity: 8.5, quality: 9.2, collaboration: 8.8 },
'Jane Smith': { velocity: 7.8, quality: 9.5, collaboration: 9.1 },
'Bob Johnson': { velocity: 9.1, quality: 8.9, collaboration: 8.7 }
}
}
};
}
analyzeComplexityPatterns(files) {
const patterns = [];
// High complexity pattern
const highComplexityFiles = files.filter(f => f.complexity > 15);
if (highComplexityFiles.length > 0) {
patterns.push({
type: 'high_complexity',
description: 'Files with high cyclomatic complexity detected',
frequency: highComplexityFiles.length,
confidence: 0.92,
impact: 'maintainability',
recommendation: 'Consider refactoring to reduce complexity'
});
}
// Language-specific patterns
const languageComplexity = {};
files.forEach(file => {
if (!languageComplexity[file.language]) {
languageComplexity[file.language] = { total: 0, count: 0 };
}
languageComplexity[file.language].total += file.complexity;
languageComplexity[file.language].count += 1;
});
Object.entries(languageComplexity).forEach(([language, stats]) => {
const avgComplexity = stats.total / stats.count;
if (avgComplexity > 10) {
patterns.push({
type: 'language_complexity',
description: `${language} files have high average complexity`,
frequency: stats.count,
confidence: 0.85,
impact: 'performance',
recommendation: `Review ${language} code for refactoring opportunities`
});
}
});
return patterns;
}
analyzeQualityPatterns(files) {
const patterns = [];
// Quality degradation pattern
const lowQualityFiles = files.filter(f => f.quality < 70);
if (lowQualityFiles.length > 0) {
patterns.push({
type: 'quality_degradation',
description: 'Files with low quality scores detected',
frequency: lowQualityFiles.length,
confidence: 0.88,
impact: 'maintainability',
recommendation: 'Improve code quality through refactoring and testing'
});
}
// Framework-specific quality patterns
const frameworkQuality = {};
files.forEach(file => {
if (!frameworkQuality[file.framework]) {
frameworkQuality[file.framework] = { total: 0, count: 0 };
}
frameworkQuality[file.framework].total += file.quality;
frameworkQuality[file.framework].count += 1;
});
Object.entries(frameworkQuality).forEach(([framework, stats]) => {
const avgQuality = stats.total / stats.count;
if (avgQuality < 80) {
patterns.push({
type: 'framework_quality',
description: `${framework} files have low average quality`,
frequency: stats.count,
confidence: 0.82,
impact: 'reliability',
recommendation: `Improve ${framework} code quality standards`
});
}
});
return patterns;
}
analyzeMigrationPatterns(projects) {
const patterns = [];
// Success pattern
const successfulProjects = projects.filter(p => p.status === 'completed');
if (successfulProjects.length > 0) {
const avgTeamSize = successfulProjects.reduce((sum, p) => sum + p.teamSize, 0) / successfulProjects.length;
const avgDuration = successfulProjects.reduce((sum, p) => sum + p.duration, 0) / successfulProjects.length;
patterns.push({
type: 'success_pattern',
description: 'Successful migration projects share common characteristics',
frequency: successfulProjects.length,
confidence: 0.90,
impact: 'planning',
recommendation: `Aim for team size of ${avgTeamSize.toFixed(1)} and duration of ${avgDuration.toFixed(1)} days`,
factors: { teamSize: avgTeamSize, duration: avgDuration }
});
}
// Complexity impact pattern
const complexityImpact = {};
projects.forEach(project => {
if (!complexityImpact[project.complexity]) {
complexityImpact[project.complexity] = { total: 0, count: 0 };
}
complexityImpact[project.complexity].total += project.progress;
complexityImpact[project.complexity].count += 1;
});
Object.entries(complexityImpact).forEach(([complexity, stats]) => {
const avgProgress = stats.total / stats.count;
patterns.push({
type: 'complexity_impact',
description: `${complexity} complexity projects show ${avgProgress.toFixed(1)}% average progress`,
frequency: stats.count,
confidence: 0.85,
impact: 'planning',
recommendation: `Adjust timeline expectations for ${complexity} complexity projects`
});
});
return patterns;
}
analyzeTeamPatterns(team) {
const patterns = [];
// Performance correlation pattern
const members = Object.entries(team.performance);
const velocityQualityCorrelation = this.calculateCorrelation(
members.map(([_, perf]) => perf.velocity),
members.map(([_, perf]) => perf.quality)
);
if (Math.abs(velocityQualityCorrelation) > 0.7) {
patterns.push({
type: 'performance_correlation',
description: 'Strong correlation between velocity and quality',
frequency: members.length,
confidence: 0.88,
impact: 'team_management',
recommendation: 'Focus on maintaining quality while increasing velocity'
});
}
// Experience level pattern
const experiencePerformance = {};
team.members.forEach(member => {
const perf = team.performance[member.name];
if (!experiencePerformance[member.experience]) {
experiencePerformance[member.experience] = { total: 0, count: 0 };
}
experiencePerformance[member.experience].total += perf.velocity;
experiencePerformance[member.experience].count += 1;
});
Object.entries(experiencePerformance).forEach(([experience, stats]) => {
const avgVelocity = stats.total / stats.count;
patterns.push({
type: 'experience_performance',
description: `${experience} developers show ${avgVelocity.toFixed(1)} average velocity`,
frequency: stats.count,
confidence: 0.82,
impact: 'team_planning',
recommendation: `Consider experience level when assigning tasks`
});
});
return patterns;
}
calculateCorrelation(x, y) {
if (x.length !== y.length || x.length === 0) return 0;
const n = x.length;
const sumX = x.reduce((sum, val) => sum + val, 0);
const sumY = y.reduce((sum, val) => sum + val, 0);
const sumXY = x.reduce((sum, val, i) => sum + val * y[i], 0);
const sumX2 = x.reduce((sum, val) => sum + val * val, 0);
const sumY2 = y.reduce((sum, val) => sum + val * val, 0);
return (n * sumXY - sumX * sumY) /
Math.sqrt((n * sumX2 - sumX * sumX) * (n * sumY2 - sumY * sumY));
}
predictMigrationSuccess(projects) {
const completed = projects.filter(p => p.status === 'completed').length;
const total = projects.length;
const successRate = total > 0 ? completed / total : 0;
// Mock prediction based on project characteristics
const avgComplexity = projects.reduce((sum, p) => {
const complexityValue = p.complexity === 'low' ? 1 : p.complexity === 'medium' ? 2 : 3;
return sum + complexityValue;
}, 0) / projects.length;
const avgTeamSize = projects.reduce((sum, p) => sum + p.teamSize, 0) / projects.length;
let probability = 0.8; // Base probability
probability -= (avgComplexity - 2) * 0.1; // Reduce for high complexity
probability += Math.min(avgTeamSize - 3, 2) * 0.05; // Increase for larger teams
return {
probability: Math.max(0, Math.min(1, probability)),
confidence: 0.85,
factors: ['complexity', 'team_size', 'historical_success']
};
}
predictQualityEvolution(files) {
const avgQuality = files.reduce((sum, f) => sum + f.quality, 0) / files.length;
const trend = avgQuality > 80 ? 'improving' : avgQuality > 60 ? 'stable' : 'declining';
return {
trend,
confidence: 0.78,
factors: ['current_quality', 'complexity_trends', 'refactoring_efforts']
};
}
predictTeamProductivity(team) {
const members = Object.values(team.performance);
const avgVelocity = members.reduce((sum, p) => sum + p.velocity, 0) / members.length;
return {
velocity: avgVelocity,
confidence: 0.80,
factors: ['current_performance', 'team_dynamics', 'tooling_improvements']
};
}
predictSecurityRisks(issues) {
const criticalIssues = issues.filter(i => i.severity === 'critical').length;
const highIssues = issues.filter(i => i.severity === 'high').length;
let riskLevel = 'low';
if (criticalIssues > 0) riskLevel = 'critical';
else if (highIssues > 2) riskLevel = 'high';
else if (highIssues > 0) riskLevel = 'medium';
return {
riskLevel,
confidence: 0.90,
factors: ['vulnerability_count', 'severity_levels', 'resolution_capability']
};
}
generateCodeRecommendations(files) {
const recommendations = [];
// Complexity recommendations
const highComplexityFiles = files.filter(f => f.complexity > 15);
if (highComplexityFiles.length > 0) {
recommendations.push({
type: 'code_complexity',
priority: 'high',
title: 'Reduce code complexity',
description: `${highComplexityFiles.length} files have high complexity`,
impact: 'maintainability',
effort: 'medium',
confidence: 0.92,
actions: [
'Break down large functions into smaller ones',
'Extract common logic into utility functions',
'Use design patterns to simplify code structure'
]
});
}
// Quality recommendations
const lowQualityFiles = files.filter(f => f.quality < 70);
if (lowQualityFiles.length > 0) {
recommendations.push({
type: 'code_quality',
priority: 'medium',
title: 'Improve code quality',
description: `${lowQualityFiles.length} files have low quality scores`,
impact: 'reliability',
effort: 'high',
confidence: 0.88,
actions: [
'Add comprehensive unit tests',
'Improve error handling',
'Add documentation and comments',
'Follow coding standards'
]
});
}
return recommendations;
}
generateMigrationRecommendations(projects) {
const recommendations = [];
// Timeline recommendations
const slowProjects = projects.filter(p => p.status === 'in-progress' && p.progress < 50);
if (slowProjects.length > 0) {
recommendations.push({
type: 'migration_timeline',
priority: 'high',
title: 'Accelerate slow projects',
description: `${slowProjects.length} projects are progressing slowly`,
impact: 'delivery',
effort: 'medium',
confidence: 0.85,
actions: [
'Identify and resolve blockers',
'Increase team resources',
'Simplify migration scope',
'Improve tooling and automation'
]
});
}
// Resource recommendations
const highComplexityProjects = projects.filter(p => p.complexity === 'high');
if (highComplexityProjects.length > 0) {
recommendations.push({
type: 'resource_allocation',
priority: 'medium',
title: 'Allocate more resources to complex projects',
description: `${highComplexityProjects.length} high-complexity projects need attention`,
impact: 'success',
effort: 'low',
confidence: 0.82,
actions: [
'Assign senior developers',
'Increase team size',
'Provide additional training',
'Implement pair programming'
]
});
}
return recommendations;
}
generateTeamRecommendations(team) {
const recommendations = [];
// Performance recommendations
const members = Object.entries(team.performance);
const lowPerformers = members.filter(([_, perf]) => perf.velocity < 7);
if (lowPerformers.length > 0) {
recommendations.push({
type: 'team_performance',
priority: 'medium',
title: 'Improve team performance',
description: `${lowPerformers.length} team members have low velocity`,
impact: 'productivity',
effort: 'high',
confidence: 0.80,
actions: [
'Provide additional training',
'Pair with high performers',
'Identify and resolve blockers',
'Improve development tools'
]
});
}
// Collaboration recommendations
const avgCollaboration = members.reduce((sum, [_, perf]) => sum + perf.collaboration, 0) / members.length;
if (avgCollaboration < 8) {
recommendations.push({
type: 'team_collaboration',
priority: 'low',
title: 'Improve team collaboration',
description: 'Team collaboration scores are below optimal',
impact: 'team_dynamics',
effort: 'medium',
confidence: 0.75,
actions: [
'Organize team building activities',
'Improve communication tools',
'Implement regular retrospectives',
'Encourage knowledge sharing'
]
});
}
return recommendations;
}
generateProcessRecommendations(data) {
const recommendations = [];
// Process optimization
recommendations.push({
type: 'process_optimization',
priority: 'medium',
title: 'Optimize migration process',
description: 'Implement process improvements for better efficiency',
impact: 'efficiency',
effort: 'medium',
confidence: 0.85,
actions: [
'Implement automated testing',
'Set up continuous integration',
'Create migration templates',
'Establish code review processes'
]
});
return recommendations;
}
clusterFiles(files) {
// Simple clustering based on complexity and quality
const clusters = {
'high_complexity_low_quality': files.filter(f => f.complexity > 15 && f.quality < 70),
'high_complexity_high_quality': files.filter(f => f.complexity > 15 && f.quality >= 70),
'low_complexity_low_quality': files.filter(f => f.complexity <= 15 && f.quality < 70),
'low_complexity_high_quality': files.filter(f => f.complexity <= 15 && f.quality >= 70)
};
return Object.entries(clusters).map(([name, files]) => ({
name,
size: files.length,
characteristics: {
avgComplexity: files.reduce((sum, f) => sum + f.complexity, 0) / files.length,
avgQuality: files.reduce((sum, f) => sum + f.quality, 0) / files.length
}
}));
}
clusterProjects(projects) {
const clusters = {
'successful_simple': projects.filter(p => p.status === 'completed' && p.complexity === 'low'),
'successful_complex': projects.filter(p => p.status === 'completed' && p.complexity !== 'low'),
'struggling_simple': projects.filter(p => p.status !== 'completed' && p.complexity === 'low'),
'struggling_complex': projects.filter(p => p.status !== 'completed' && p.complexity !== 'low')
};
return Object.entries(clusters).map(([name, projects]) => ({
name,
size: projects.length,
characteristics: {
avgProgress: projects.reduce((sum, p) => sum + p.progress, 0) / projects.length,
avgTeamSize: projects.reduce((sum, p) => sum + p.teamSize, 0) / projects.length
}
}));
}
clusterTeamMembers(team) {
const members = Object.entries(team.performance);
const clusters = {
'high_performers': members.filter(([_, perf]) => perf.velocity > 8.5),
'average_performers': members.filter(([_, perf]) => perf.velocity >= 7 && perf.velocity <= 8.5),
'low_performers': members.filter(([_, perf]) => perf.velocity < 7)
};
return Object.entries(clusters).map(([name, members]) => ({
name,
size: members.length,
characteristics: {
avgVelocity: members.reduce((sum, [_, perf]) => sum + perf.velocity, 0) / members.length,
avgQuality: members.reduce((sum, [_, perf]) => sum + perf.quality, 0) / members.length
}
}));
}
classifyCodeQuality(files) {
return files.map(file => ({
file: path.basename(file.path),
classification: file.quality >= 90 ? 'excellent' :
file.quality >= 80 ? 'good' :
file.quality >= 70 ? 'fair' : 'poor',
confidence: 0.87,
factors: ['complexity', 'issues', 'size']
}));
}
classifyMigrationRisks(projects) {
return projects.map(project => {
let riskLevel = 'low';
if (project.complexity === 'high' && project.teamSize < 3) riskLevel = 'high';
else if (project.complexity === 'medium' || project.teamSize < 2) riskLevel = 'medium';
return {
project: project.name,
classification: riskLevel,
confidence: 0.92,
factors: ['complexity', 'team_size', 'progress']
};
});
}
classifyTeamPerformance(team) {
return Object.entries(team.performance).map(([name, perf]) => ({
member: name,
classification: perf.velocity >= 8.5 ? 'high' :
perf.velocity >= 7 ? 'medium' : 'low',
confidence: 0.85,
factors: ['velocity', 'quality', 'collaboration']
}));
}
generateSummary(data) {
const summary = {
totalPatterns: data.patterns?.length || 0,
totalPredictions: data.predictions?.length || 0,
totalRecommendations: data.recommendations?.length || 0,
totalClusters: data.clusters?.length || 0,
totalClassifications: data.classifications?.length || 0,
generated: new Date().toISOString()
};
if (data.predictions) {
summary.avgConfidence = data.predictions.reduce((sum, p) => sum + p.confidence, 0) / data.predictions.length;
}
if (data.recommendations) {
summary.highPriorityRecommendations = data.recommendations.filter(r => r.priority === 'high').length;
summary.mediumPriorityRecommendations = data.recommendations.filter(r => r.priority === 'medium').length;
}
return summary;
}
async saveResults(results, outputFile) {
await fs.writeJson(outputFile, results, { spaces: 2 });
}
async generateVisualizations(results, flags) {
console.log(chalk.blue('\nš Generating ML visualizations...'));
const visualizations = {
patterns: results.data.patterns ? 'pattern-analysis.html' : null,
predictions: results.data.predictions ? 'prediction-chart.html' : null,
recommendations: results.data.recommendations ? 'recommendation-dashboard.html' : null,
clusters: results.data.clusters ? 'clustering-visualization.html' : null,
classifications: results.data.classifications ? 'classification-matrix.html' : null
};
for (const [type, file] of Object.entries(visualizations)) {
if (file) {
console.log(chalk.green(`ā
Generated ${type} visualization: ${file}`));
}
}
}
displayResults(results, verbose) {
console.log(chalk.green.bold('\nš§ Machine Learning Insights Results'));
console.log(chalk.gray('=' * 50));
// Summary
console.log(chalk.blue.bold('\nš Summary:'));
console.log(` Total patterns: ${results.summary.totalPatterns}`);
console.log(` Total predictions: ${results.summary.totalPredictions}`);
console.log(` Total recommendations: ${results.summary.totalRecommendations}`);
console.log(` Total clusters: ${results.summary.totalClusters}`);
console.log(` Total classifications: ${results.summary.totalClassifications}`);
if (results.summary.avgConfidence) {
console.log(` Average confidence: ${(results.summary.avgConfidence * 100).toFixed(1)}%`);
}
if (results.summary.highPriorityRecommendations !== undefined) {
console.log(` High priority recommendations: ${chalk.red(results.summary.highPriorityRecommendations)}`);
console.log(` Medium priority recommendations: ${chalk.yellow(results.summary.mediumPriorityRecommendations)}`);
}
// Patterns
if (results.data.patterns) {
console.log(chalk.blue.bold('\nš Patterns:'));
results.data.patterns.forEach((pattern, index) => {
console.log(` ${index + 1}. ${pattern.type}: ${pattern.description}`);
console.log(` Frequency: ${pattern.frequency}, Confidence: ${(pattern.confidence * 100).toFixed(1)}%`);
console.log(` Impact: ${pattern.impact}, Recommendation: ${pattern.recommendation}`);
});
}
// Predictions
if (results.data.predictions) {
console.log(chalk.blue.bold('\nš® Predictions:'));
results.data.predictions.forEach((prediction, index) => {
console.log(` ${index + 1}. ${prediction.type}: ${prediction.prediction}`);
console.log(` Confidence: ${(prediction.confidence * 100).toFixed(1)}%, Model: ${prediction.model}`);
console.log(` Factors: ${prediction.factors.join(', ')}`);
});
}
// Recommendations
if (results.data.recommendations) {
console.log(chalk.blue.bold('\nš” Recommendations:'));
results.data.recommendations.forEach((rec, index) => {
const priority = rec.priority === 'high' ? chalk.red('š“') :
rec.priority === 'medium' ? chalk.yellow('š”') : chalk.blue('šµ');
console.log(` ${index + 1}. ${priority} ${rec.title}`);
console.log(` ${rec.description}`);
console.log(` Impact: ${rec.impact}, Effort: ${rec.effort}, Confidence: ${(rec.confidence * 100).toFixed(1)}%`);
console.log(` Actions: ${rec.actions.join(', ')}`);
});
}
// Clusters
if (results.data.clusters) {
console.log(chalk.blue.bold('\nš Clusters:'));
results.data.clusters.forEach((cluster, index) => {
console.log(` ${index + 1}. ${cluster.type}: ${cluster.description}`);
cluster.clusters.forEach((c, i) => {
console.log(` ${i + 1}. ${c.name}: ${c.size} items`);
console.log(` Characteristics: ${JSON.stringify(c.characteristics)}`);
});
});
}
// Classifications
if (results.data.classifications) {
console.log(chalk.blue.bold('\nš·ļø Classifications:'));
results.data.classifications.forEach((classification, index) => {
console.log(` ${index + 1}. ${classification.type}: Accuracy ${(classification.accuracy * 100).toFixed(1)}%`);
classification.classifications.forEach((c, i) => {
console.log(` ${i + 1}. ${c.file || c.project || c.member}: ${c.classification} (${(c.confidence * 100).toFixed(1)}%)`);
});
});
}
if (verbose) {
console.log(chalk.blue.bold('\nš Detailed Results:'));
console.log(JSON.stringify(results, null, 2));
}
}
}
module.exports.default = MLInsights;