MCP Code Analysis & Quality Server

// Copyright 2025 Chris Bunting // Brief: Context-aware analysis engine for MCP Code Analysis & Quality Server // Scope: Provides intelligent analysis based on project context, team preferences, and historical data import { EventEmitter } from 'events'; import { UnifiedAnalysisResult, AnalysisContext, AnalysisIssue, UnifiedIssue, SeverityLevel, PriorityLevel, IssueCategory, Location, ImpactAssessment, CrossReference, CorrelationData, SuggestionData, PatternData, TeamContext, UserPreferences, HistoricalData, QualityTrend, PerformanceData, TrendDirection, PatternType, CorrelationType, SuggestionType, ProjectType, DevelopmentStage, ExperienceLevel, CollaborationStyle, AnalysisDepth, LoggerInterface, CacheInterface } from '@mcp-code-analysis/shared-types'; export interface ContextAnalysisConfig { enableLearning: boolean; enablePatternRecognition: boolean; enableCorrelationAnalysis: boolean; enableImpactAssessment: boolean; maxHistoricalData: number; patternThreshold: number; correlationThreshold: number; learningRate: number; contextWeight: { projectType: number; developmentStage: number; teamExperience: number; historicalPerformance: number; userPreferences: number; }; } export interface ContextAnalysisRequest { projectId: string; analysisResults: UnifiedAnalysisResult[]; context: AnalysisContext; options: ContextAnalysisOptions; } export interface ContextAnalysisOptions { includePatterns: boolean; includeCorrelations: boolean; includeImpactAssessment: boolean; includeSuggestions: boolean; depth: AnalysisDepth; focusAreas: FocusArea[]; } export interface FocusArea { type: FocusType; priority: PriorityLevel; criteria: FocusCriteria[]; } export interface FocusCriteria { field: string; operator: FilterOperator; value: any; } export interface ContextAnalysisResult { id: string; projectId: string; timestamp: Date; originalResults: UnifiedAnalysisResult[]; enhancedResults: EnhancedAnalysisResult[]; patterns: ContextPattern[]; correlations: ContextCorrelation[]; impactAssessment: ContextImpactAssessment; suggestions: ContextSuggestion[]; insights: ContextInsight[]; metadata: AnalysisMetadata; } export interface EnhancedAnalysisResult extends UnifiedAnalysisResult { contextScore: number; relevanceScore: number; priorityAdjustment: number; contextualFactors: ContextualFactor[]; teamConsiderations: TeamConsideration[]; historicalContext: HistoricalContext[]; } export interface ContextualFactor { type: FactorType; description: string; impact: number; confidence: number; evidence: string[]; } export interface TeamConsideration { aspect: TeamAspect; recommendation: string; priority: PriorityLevel; effort: number; expectedOutcome: string; } export interface HistoricalContext { timeframe: Timeframe; pattern: string; frequency: number; trend: TrendDirection; impact: number; } export interface ContextPattern { id: string; type: PatternType; description: string; confidence: number; frequency: number; impact: number; locations: Location[]; context: PatternContext; suggestions: string[]; } export interface PatternContext { projectType: ProjectType; developmentStage: DevelopmentStage; teamExperience: ExperienceLevel; technologyFactors: string[]; businessFactors: string[]; } export interface ContextCorrelation { id: string; type: CorrelationType; description: string; confidence: number; strength: number; relatedIssues: string[]; causalFactors: string[]; context: CorrelationContext; } export interface CorrelationContext { temporal: TemporalContext; spatial: SpatialContext; technical: TechnicalContext; team: TeamContext; } export interface TemporalContext { timeframe: Timeframe; frequency: number; trend: TrendDirection; } export interface SpatialContext { files: string[]; modules: string[]; components: string[]; } export interface TechnicalContext { technologies: string[]; patterns: string[]; dependencies: string[]; } export interface ContextImpactAssessment { overallImpact: ImpactAssessment; categoryImpacts: CategoryImpact[]; teamImpact: TeamImpact; businessImpact: BusinessImpact; technicalImpact: TechnicalImpact; } export interface CategoryImpact { category: IssueCategory; impact: number; priority: PriorityLevel; rationale: string; } export interface TeamImpact { productivity: number; morale: number; learning: number; collaboration: number; } export interface BusinessImpact { revenue: number; cost: number; risk: number; opportunity: number; } export interface TechnicalImpact { maintainability: number; scalability: number; performance: number; security: number; } export interface ContextSuggestion extends SuggestionData { contextRelevance: number; teamAlignment: number; historicalSuccess: number; implementationComplexity: number; expectedROI: number; contextualFactors: string[]; } export interface ContextInsight { id: string; type: InsightType; title: string; description: string; confidence: number; impact: ImpactAssessment; evidence: string[]; recommendations: string[]; context: InsightContext; } export interface InsightContext { projectContext: AnalysisContext; teamContext: TeamContext; historicalContext: HistoricalData; marketContext: MarketContext; } export interface MarketContext { industry: string; competition: string; trends: string[]; regulations: string[]; } export interface AnalysisMetadata { generatedAt: Date; generationTime: number; analysisDepth: AnalysisDepth; patternsFound: number; correlationsFound: number; suggestionsGenerated: number; insightsGenerated: number; contextFactors: ContextFactor[]; } export enum FactorType { PROJECT_TYPE = 'project-type', DEVELOPMENT_STAGE = 'development-stage', TEAM_EXPERIENCE = 'team-experience', TECHNOLOGY_STACK = 'technology-stack', BUSINESS_DOMAIN = 'business-domain', REGULATORY_REQUIREMENTS = 'regulatory-requirements', PERFORMANCE_REQUIREMENTS = 'performance-requirements', SECURITY_REQUIREMENTS = 'security-requirements' } export enum TeamAspect { SKILL_LEVEL = 'skill-level', CODING_STANDARDS = 'coding-standards', COLLABORATION_STYLE = 'collaboration-style', PREFERRED_PRACTICES = 'preferred-practices', LEARNING_APTITUDE = 'learning-aptitude', COMMUNICATION_PATTERNS = 'communication-patterns' } export enum FocusType { PERFORMANCE = 'performance', SECURITY = 'security', MAINTAINABILITY = 'maintainability', SCALABILITY = 'scalability', USABILITY = 'usability', COMPLIANCE = 'compliance' } export enum FilterOperator { EQUALS = 'equals', NOT_EQUALS = 'not-equals', GREATER_THAN = 'greater-than', LESS_THAN = 'less-than', CONTAINS = 'contains', IN = 'in', MATCHES = 'matches' } export enum InsightType { QUALITY_TREND = 'quality-trend', TEAM_PATTERN = 'team-pattern', TECHNOLOGY_RISK = 'technology-risk', BUSINESS_IMPACT = 'business-impact', PROCESS_IMPROVEMENT = 'process-improvement', SKILL_DEVELOPMENT = 'skill-development' } export class ContextAwareAnalysisEngine extends EventEmitter { private config: ContextAnalysisConfig; private cache: CacheInterface; private logger: LoggerInterface; private patternDatabase: Map<string, ContextPattern[]> = new Map(); private correlationDatabase: Map<string, ContextCorrelation[]> = new Map(); private learningData: Map<string, LearningData> = new Map(); constructor(config: ContextAnalysisConfig, cache: CacheInterface, logger: LoggerInterface) { super(); this.config = config; this.cache = cache; this.logger = logger; this.setupEventHandlers(); } private setupEventHandlers(): void { this.on('pattern-discovered', this.handlePatternDiscovered.bind(this)); this.on('correlation-found', this.handleCorrelationFound.bind(this)); this.on('insight-generated', this.handleInsightGenerated.bind(this)); this.on('context-updated', this.handleContextUpdated.bind(this)); } async initialize(): Promise<void> { this.logger.info('Initializing Context-Aware Analysis Engine'); // Load pattern and correlation databases await this.loadDatabases(); // Load learning data await this.loadLearningData(); this.logger.info('Context-Aware Analysis Engine initialized successfully'); } async analyzeWithContext(request: ContextAnalysisRequest): Promise<ContextAnalysisResult> { try { this.logger.info(`Starting context-aware analysis for project: ${request.projectId}`); const startTime = Date.now(); // Enhance analysis results with context const enhancedResults = await this.enhanceAnalysisResults(request); // Discover patterns const patterns = request.options.includePatterns ? await this.discoverPatterns(request, enhancedResults) : []; // Find correlations const correlations = request.options.includeCorrelations ? await this.findCorrelations(request, enhancedResults) : []; // Assess impact const impactAssessment = request.options.includeImpactAssessment ? await this.assessImpact(request, enhancedResults, patterns, correlations) : this.createDefaultImpactAssessment(); // Generate suggestions const suggestions = request.options.includeSuggestions ? await this.generateSuggestions(request, enhancedResults, patterns, correlations, impactAssessment) : []; // Generate insights const insights = await this.generateInsights(request, enhancedResults, patterns, correlations, impactAssessment); // Create metadata const metadata = await this.createAnalysisMetadata(request, Date.now() - startTime); // Assemble final result const result: ContextAnalysisResult = { id: this.generateAnalysisId(), projectId: request.projectId, timestamp: new Date(), originalResults: request.analysisResults, enhancedResults, patterns, correlations, impactAssessment, suggestions, insights, metadata }; // Update learning data if enabled if (this.config.enableLearning) { await this.updateLearningData(request, result); } // Emit event this.emit('analysis-completed', result); this.logger.info(`Context-aware analysis completed in ${Date.now() - startTime}ms`); return result; } catch (error) { this.logger.error('Failed to perform context-aware analysis:', error); throw error; } } private async enhanceAnalysisResults(request: ContextAnalysisRequest): Promise<EnhancedAnalysisResult[]> { const enhancedResults: EnhancedAnalysisResult[] = []; for (const result of request.analysisResults) { const enhanced = await this.enhanceSingleResult(result, request.context); enhancedResults.push(enhanced); } return enhancedResults; } private async enhanceSingleResult(result: UnifiedAnalysisResult, context: AnalysisContext): Promise<EnhancedAnalysisResult> { // Calculate context score const contextScore = await this.calculateContextScore(result, context); // Calculate relevance score const relevanceScore = await this.calculateRelevanceScore(result, context); // Adjust priority based on context const priorityAdjustment = await this.calculatePriorityAdjustment(result, context); // Identify contextual factors const contextualFactors = await this.identifyContextualFactors(result, context); // Consider team aspects const teamConsiderations = await this.generateTeamConsiderations(result, context); // Provide historical context const historicalContext = await this.generateHistoricalContext(result, context); return { ...result, contextScore, relevanceScore, priorityAdjustment, contextualFactors, teamConsiderations, historicalContext }; } private async calculateContextScore(result: UnifiedAnalysisResult, context: AnalysisContext): Promise<number> { let score = 0; const weights = this.config.contextWeight; // Project type relevance score += this.getProjectTypeRelevance(result, context.projectType) * weights.projectType; // Development stage relevance score += this.getDevelopmentStageRelevance(result, context.developmentStage) * weights.developmentStage; // Team experience relevance score += this.getTeamExperienceRelevance(result, context.teamContext.experienceLevel) * weights.teamExperience; // Historical performance relevance score += this.getHistoricalPerformanceRelevance(result, context.historicalData) * weights.historicalPerformance; // User preferences relevance score += this.getUserPreferencesRelevance(result, context.userPreferences) * weights.userPreferences; return Math.min(100, Math.max(0, score)); } private async calculateRelevanceScore(result: UnifiedAnalysisResult, context: AnalysisContext): Promise<number> { // Calculate relevance based on current focus areas and project priorities let relevance = 50; // Base relevance // Adjust based on issue categories and project priorities const categoryRelevance = this.getCategoryRelevance(result.issues, context); relevance += categoryRelevance * 0.3; // Adjust based on severity and project stage const severityRelevance = this.getSeverityRelevance(result.issues, context.developmentStage); relevance += severityRelevance * 0.3; // Adjust based on technology stack alignment const techRelevance = this.getTechnologyRelevance(result, context.technologyStack); relevance += techRelevance * 0.2; // Adjust based on team capabilities const teamRelevance = this.getTeamRelevance(result, context.teamContext); relevance += teamRelevance * 0.2; return Math.min(100, Math.max(0, relevance)); } private async calculatePriorityAdjustment(result: UnifiedAnalysisResult, context: AnalysisContext): Promise<number> { let adjustment = 0; // Adjust based on development stage switch (context.developmentStage) { case DevelopmentStage.PLANNING: adjustment -= 0.2; // Lower priority in planning break; case DevelopmentStage.DEVELOPMENT: adjustment += 0.1; // Higher priority during development break; case DevelopmentStage.TESTING: adjustment += 0.3; // Much higher priority during testing break; case DevelopmentStage.DEPLOYMENT: adjustment += 0.4; // Highest priority during deployment break; case DevelopmentStage.MAINTENANCE: adjustment += 0.2; // Higher priority in maintenance break; } // Adjust based on team experience switch (context.teamContext.experienceLevel) { case ExperienceLevel.JUNIOR: adjustment += 0.2; // Higher priority for junior teams break; case ExperienceLevel.EXPERT: adjustment -= 0.1; // Lower priority for expert teams break; } // Adjust based on business criticality if (context.projectType === ProjectType.API || context.projectType === ProjectType.MICROSERVICE) { adjustment += 0.2; } return adjustment; } private async identifyContextualFactors(result: UnifiedAnalysisResult, context: AnalysisContext): Promise<ContextualFactor[]> { const factors: ContextualFactor[] = []; // Project type factors factors.push({ type: FactorType.PROJECT_TYPE, description: `Analysis tailored for ${context.projectType} project`, impact: 0.8, confidence: 0.9, evidence: ['Project type classification', 'Technology stack analysis'] }); // Development stage factors factors.push({ type: FactorType.DEVELOPMENT_STAGE, description: `Considerations for ${context.developmentStage} stage`, impact: 0.7, confidence: 0.85, evidence: ['Development lifecycle stage', 'Team workflow analysis'] }); // Team experience factors factors.push({ type: FactorType.TEAM_EXPERIENCE, description: `Adjusted for ${context.teamContext.experienceLevel} experience level`, impact: 0.6, confidence: 0.8, evidence: ['Team skill assessment', 'Historical performance data'] }); // Technology stack factors factors.push({ type: FactorType.TECHNOLOGY_STACK, description: `Analysis considers ${context.technologyStack.join(', ')} technologies`, impact: 0.75, confidence: 0.9, evidence: ['Technology dependency analysis', 'Framework-specific patterns'] }); return factors; } private async generateTeamConsiderations(result: UnifiedAnalysisResult, context: AnalysisContext): Promise<TeamConsideration[]> { const considerations: TeamConsideration[] = []; // Skill level considerations if (context.teamContext.experienceLevel === ExperienceLevel.JUNIOR) { considerations.push({ aspect: TeamAspect.SKILL_LEVEL, recommendation: 'Provide detailed documentation and pair programming opportunities', priority: PriorityLevel.HIGH, effort: 3, expectedOutcome: 'Improved code quality and faster onboarding' }); } // Coding standards considerations considerations.push({ aspect: TeamAspect.CODING_STANDARDS, recommendation: 'Ensure adherence to team coding standards and best practices', priority: PriorityLevel.MEDIUM, effort: 2, expectedOutcome: 'Consistent code quality and maintainability' }); // Collaboration style considerations if (context.teamContext.collaborationStyle === CollaborationStyle.CODE_REVIEW) { considerations.push({ aspect: TeamAspect.COLLABORATION_STYLE, recommendation: 'Leverage code review process for knowledge sharing', priority: PriorityLevel.MEDIUM, effort: 2, expectedOutcome: 'Improved code quality and team knowledge' }); } return considerations; } private async generateHistoricalContext(result: UnifiedAnalysisResult, context: AnalysisContext): Promise<HistoricalContext[]> { const historicalContext: HistoricalContext[] = []; // Analyze historical trends for (const trend of context.historicalData.qualityTrends) { historicalContext.push({ timeframe: Timeframe.MONTH, pattern: trend.metric, frequency: 1, // Default frequency since we don't have the actual data structure trend: trend.trend, impact: this.calculateTrendImpact(trend) }); } // Analyze performance history for (const perf of context.historicalData.performanceHistory) { historicalContext.push({ timeframe: Timeframe.MONTH, pattern: perf.metric, frequency: 1, // Default frequency since we don't have the actual data structure trend: perf.deviation > 0 ? TrendDirection.DECLINING : TrendDirection.IMPROVING, impact: Math.abs(perf.deviation) }); } return historicalContext; } private async discoverPatterns(request: ContextAnalysisRequest, results: EnhancedAnalysisResult[]): Promise<ContextPattern[]> { const patterns: ContextPattern[] = []; // Analyze patterns across all results const allIssues = results.flatMap(r => r.issues); // Discover recurring issue patterns const issuePatterns = await this.discoverIssuePatterns(allIssues, request.context); patterns.push(...issuePatterns); // Discover technology-specific patterns const techPatterns = await this.discoverTechnologyPatterns(results, request.context); patterns.push(...techPatterns); // Discover team-specific patterns const teamPatterns = await this.discoverTeamPatterns(results, request.context); patterns.push(...teamPatterns); // Filter patterns by threshold return patterns.filter(p => p.confidence >= this.config.patternThreshold); } private async discoverIssuePatterns(issues: UnifiedIssue[], context: AnalysisContext): Promise<ContextPattern[]> { const patterns: ContextPattern[] = []; // Group issues by category and analyze patterns const categoryGroups = this.groupIssuesByCategory(issues); for (const [category, categoryIssues] of categoryGroups) { if (categoryIssues.length >= 3) { // Minimum threshold for pattern const pattern: ContextPattern = { id: this.generatePatternId(), type: PatternType.CODE_SMELL, description: `Recurring ${category} issues detected`, confidence: this.calculatePatternConfidence(categoryIssues), frequency: categoryIssues.length, impact: this.calculatePatternImpact(categoryIssues), locations: categoryIssues.map(i => i.location), context: { projectType: context.projectType, developmentStage: context.developmentStage, teamExperience: context.teamContext.experienceLevel, technologyFactors: context.technologyStack, businessFactors: [] }, suggestions: [ 'Implement code review checkpoints', 'Add automated testing for common issues', 'Provide team training on best practices' ] }; patterns.push(pattern); } } return patterns; } private async discoverTechnologyPatterns(results: EnhancedAnalysisResult[], context: AnalysisContext): Promise<ContextPattern[]> { const patterns: ContextPattern[] = []; // Analyze patterns specific to the technology stack for (const technology of context.technologyStack) { const techIssues = results.flatMap(r => r.issues).filter(issue => this.isTechnologyRelated(issue, technology) ); if (techIssues.length >= 2) { const pattern: ContextPattern = { id: this.generatePatternId(), type: PatternType.ANTI_PATTERN, description: `${technology}-specific anti-patterns detected`, confidence: this.calculatePatternConfidence(techIssues), frequency: techIssues.length, impact: this.calculatePatternImpact(techIssues), locations: techIssues.map(i => i.location), context: { projectType: context.projectType, developmentStage: context.developmentStage, teamExperience: context.teamContext.experienceLevel, technologyFactors: [technology], businessFactors: [] }, suggestions: [ `Review ${technology} best practices`, `Implement ${technology}-specific linting rules`, `Consider refactoring problematic areas` ] }; patterns.push(pattern); } } return patterns; } private async discoverTeamPatterns(results: EnhancedAnalysisResult[], context: AnalysisContext): Promise<ContextPattern[]> { const patterns: ContextPattern[] = []; // Analyze patterns based on team collaboration style const collaborationIssues = results.flatMap(r => r.issues).filter(issue => this.isCollaborationRelated(issue, context.teamContext.collaborationStyle) ); if (collaborationIssues.length >= 2) { const pattern: ContextPattern = { id: this.generatePatternId(), type: PatternType.BUG_PATTERN, description: `Collaboration-related issues in ${context.teamContext.collaborationStyle} style`, confidence: this.calculatePatternConfidence(collaborationIssues), frequency: collaborationIssues.length, impact: this.calculatePatternImpact(collaborationIssues), locations: collaborationIssues.map(i => i.location), context: { projectType: context.projectType, developmentStage: context.developmentStage, teamExperience: context.teamContext.experienceLevel, technologyFactors: context.technologyStack, businessFactors: [] }, suggestions: [ 'Improve communication protocols', 'Establish clear code ownership', 'Implement better documentation practices' ] }; patterns.push(pattern); } return patterns; } private async findCorrelations(request: ContextAnalysisRequest, results: EnhancedAnalysisResult[]): Promise<ContextCorrelation[]> { const correlations: ContextCorrelation[] = []; // Find temporal correlations const temporalCorrelations = await this.findTemporalCorrelations(results, request.context); correlations.push(...temporalCorrelations); // Find spatial correlations const spatialCorrelations = await this.findSpatialCorrelations(results, request.context); correlations.push(...spatialCorrelations); // Find technical correlations const technicalCorrelations = await this.findTechnicalCorrelations(results, request.context); correlations.push(...technicalCorrelations); // Filter correlations by threshold return correlations.filter(c => c.confidence >= this.config.correlationThreshold); } private async findTemporalCorrelations(results: EnhancedAnalysisResult[], context: AnalysisContext): Promise<ContextCorrelation[]> { const correlations: ContextCorrelation[] = []; // Analyze temporal patterns in issues const allIssues = results.flatMap(r => r.issues); // Group issues by time patterns const timeGroups = this.groupIssuesByTimePattern(allIssues); for (const [pattern, issues] of timeGroups) { if (issues.length >= 3) { const correlation: ContextCorrelation = { id: this.generateCorrelationId(), type: CorrelationType.TEMPORAL, description: `Temporal correlation: ${pattern}`, confidence: this.calculateCorrelationConfidence(issues), strength: issues.length / allIssues.length, relatedIssues: issues.map(i => i.id), causalFactors: ['Development cycle', 'Team workload', 'Deadline pressure'], context: { temporal: { timeframe: Timeframe.WEEK, frequency: issues.length, trend: TrendDirection.STABLE }, spatial: { files: issues.map(i => i.location.filePath), modules: [], components: [] }, technical: { technologies: context.technologyStack, patterns: [], dependencies: [] }, team: context.teamContext } }; correlations.push(correlation); } } return correlations; } private async findSpatialCorrelations(results: EnhancedAnalysisResult[], context: AnalysisContext): Promise<ContextCorrelation[]> { const correlations: ContextCorrelation[] = []; // Analyze spatial patterns in issues const allIssues = results.flatMap(r => r.issues); // Group issues by file/module const fileGroups = this.groupIssuesByFile(allIssues); for (const [filePath, issues] of fileGroups) { if (issues.length >= 3) { const correlation: ContextCorrelation = { id: this.generateCorrelationId(), type: CorrelationType.CORRELATED, description: `Spatial correlation in ${filePath}`, confidence: this.calculateCorrelationConfidence(issues), strength: issues.length / allIssues.length, relatedIssues: issues.map(i => i.id), causalFactors: ['File complexity', 'Code ownership', 'Module coupling'], context: { temporal: { timeframe: Timeframe.MONTH, frequency: issues.length, trend: TrendDirection.STABLE }, spatial: { files: [filePath], modules: this.extractModules(filePath), components: this.extractComponents(filePath) }, technical: { technologies: context.technologyStack, patterns: [], dependencies: [] }, team: context.teamContext } }; correlations.push(correlation); } } return correlations; } private async findTechnicalCorrelations(results: EnhancedAnalysisResult[], context: AnalysisContext): Promise<ContextCorrelation[]> { const correlations: ContextCorrelation[] = []; // Analyze technical correlations between issues const allIssues = results.flatMap(r => r.issues); // Group issues by technology const techGroups = this.groupIssuesByTechnology(allIssues, context.technologyStack); for (const [technology, issues] of techGroups) { if (issues.length >= 2) { const correlation: ContextCorrelation = { id: this.generateCorrelationId(), type: CorrelationType.CORRELATED, description: `Technical correlation with ${technology}`, confidence: this.calculateCorrelationConfidence(issues), strength: issues.length / allIssues.length, relatedIssues: issues.map(i => i.id), causalFactors: ['Technology complexity', 'Framework limitations', 'Integration challenges'], context: { temporal: { timeframe: Timeframe.MONTH, frequency: issues.length, trend: TrendDirection.STABLE }, spatial: { files: issues.map(i => i.location.filePath), modules: [], components: [] }, technical: { technologies: [technology], patterns: [], dependencies: [] }, team: context.teamContext } }; correlations.push(correlation); } } return correlations; } private async assessImpact( request: ContextAnalysisRequest, results: EnhancedAnalysisResult[], patterns: ContextPattern[], correlations: ContextCorrelation[] ): Promise<ContextImpactAssessment> { // Calculate overall impact const overallImpact = await this.calculateOverallImpact(results, patterns, correlations); // Calculate category impacts const categoryImpacts = await this.calculateCategoryImpacts(results); // Calculate team impact const teamImpact = await this.calculateTeamImpact(results, request.context); // Calculate business impact const businessImpact = await this.calculateBusinessImpact(results, request.context); // Calculate technical impact const technicalImpact = await this.calculateTechnicalImpact(results, patterns); return { overallImpact, categoryImpacts, teamImpact, businessImpact, technicalImpact }; } private async calculateOverallImpact( results: EnhancedAnalysisResult[], patterns: ContextPattern[], correlations: ContextCorrelation[] ): Promise<ImpactAssessment> { const totalIssues = results.reduce((sum, r) => sum + r.issues.length, 0); const criticalIssues = results.reduce((sum, r) => sum + r.issues.filter(i => i.severity === SeverityLevel.ERROR).length, 0 ); const patternImpact = patterns.reduce((sum, p) => sum + p.impact, 0) / patterns.length || 0; const correlationImpact = correlations.reduce((sum, c) => sum + c.strength, 0) / correlations.length || 0; const businessImpact = (criticalIssues / totalIssues) * 10 + patternImpact * 3 + correlationImpact * 2; const technicalImpact = (totalIssues / results.length) * 5 + patternImpact * 4 + correlationImpact * 3; const userImpact = Math.min(businessImpact, technicalImpact) * 0.8; const priorityScore = (businessImpact + technicalImpact + userImpact) / 3; return { businessImpact: Math.min(10, businessImpact), technicalImpact: Math.min(10, technicalImpact), userImpact: Math.min(10, userImpact), priorityScore: Math.min(10, priorityScore), effortEstimate: Math.ceil(totalIssues * 0.5), riskLevel: this.calculateRiskLevel(criticalIssues, totalIssues) }; } private async calculateCategoryImpacts(results: EnhancedAnalysisResult[]): Promise<CategoryImpact[]> { const categoryMap = new Map<IssueCategory, { count: number; severitySum: number }>(); // Aggregate issues by category for (const result of results) { for (const issue of result.issues) { const current = categoryMap.get(issue.category) || { count: 0, severitySum: 0 }; current.count++; current.severitySum += this.severityToWeight(issue.severity); categoryMap.set(issue.category, current); } } // Calculate impacts for each category const impacts: CategoryImpact[] = []; for (const [category, data] of categoryMap) { const impact = (data.count / results.length) * 10 + (data.severitySum / data.count) * 5; impacts.push({ category, impact: Math.min(10, impact), priority: this.impactToPriority(impact), rationale: `${data.count} issues with average severity ${data.severitySum / data.count}` }); } return impacts; } private async calculateTeamImpact(results: EnhancedAnalysisResult[], context: AnalysisContext): Promise<TeamImpact> { const totalIssues = results.reduce((sum, r) => sum + r.issues.length, 0); const avgContextScore = results.reduce((sum, r) => sum + r.contextScore, 0) / results.length; // Calculate impact on productivity const productivity = Math.max(0, 10 - (totalIssues * 0.1) - (10 - avgContextScore) * 0.05); // Calculate impact on morale const morale = Math.max(0, 8 - (totalIssues * 0.05) - (10 - avgContextScore) * 0.1); // Calculate impact on learning const learning = Math.min(10, totalIssues * 0.2 + avgContextScore * 0.1); // Calculate impact on collaboration const collaboration = Math.max(0, 9 - (totalIssues * 0.08) - (10 - avgContextScore) * 0.07); return { productivity, morale, learning, collaboration }; } private async calculateBusinessImpact(results: EnhancedAnalysisResult[], context: AnalysisContext): Promise<BusinessImpact> { const totalIssues = results.reduce((sum, r) => sum + r.issues.length, 0); const criticalIssues = results.reduce((sum, r) => sum + r.issues.filter(i => i.severity === SeverityLevel.ERROR).length, 0 ); // Calculate revenue impact const revenue = Math.max(0, 10 - (criticalIssues * 0.5) - (totalIssues * 0.1)); // Calculate cost impact const cost = Math.min(10, totalIssues * 0.3 + criticalIssues * 0.7); // Calculate risk impact const risk = Math.min(10, criticalIssues * 0.8 + (totalIssues * 0.2)); // Calculate opportunity impact const opportunity = Math.max(0, 10 - (totalIssues * 0.15) - (criticalIssues * 0.3)); return { revenue, cost, risk, opportunity }; } private async calculateTechnicalImpact(results: EnhancedAnalysisResult[], patterns: ContextPattern[]): Promise<TechnicalImpact> { const totalIssues = results.reduce((sum, r) => sum + r.issues.length, 0); const patternImpact = patterns.reduce((sum, p) => sum + p.impact, 0) / patterns.length || 0; // Calculate maintainability impact const maintainability = Math.max(0, 10 - (totalIssues * 0.2) - (patternImpact * 0.3)); // Calculate scalability impact const scalability = Math.max(0, 9 - (totalIssues * 0.15) - (patternImpact * 0.25)); // Calculate performance impact const performance = Math.max(0, 8 - (totalIssues * 0.1) - (patternImpact * 0.2)); // Calculate security impact const securityIssues = results.reduce((sum, r) => sum + r.issues.filter(i => i.category === IssueCategory.SECURITY).length, 0 ); const security = Math.max(0, 10 - (securityIssues * 0.8) - (patternImpact * 0.4)); return { maintainability, scalability, performance, security }; } private async generateSuggestions( request: ContextAnalysisRequest, results: EnhancedAnalysisResult[], patterns: ContextPattern[], correlations: ContextCorrelation[], impactAssessment: ContextImpactAssessment ): Promise<ContextSuggestion[]> { const suggestions: ContextSuggestion[] = []; // Generate suggestions based on patterns const patternSuggestions = await this.generatePatternSuggestions(patterns, request.context); suggestions.push(...patternSuggestions); // Generate suggestions based on correlations const correlationSuggestions = await this.generateCorrelationSuggestions(correlations, request.context); suggestions.push(...correlationSuggestions); // Generate suggestions based on impact assessment const impactSuggestions = await this.generateImpactSuggestions(impactAssessment, request.context); suggestions.push(...impactSuggestions); // Generate suggestions based on team context const teamSuggestions = await this.generateTeamSuggestions(results, request.context); suggestions.push(...teamSuggestions); // Sort by expected ROI suggestions.sort((a, b) => b.expectedROI - a.expectedROI); return suggestions.slice(0, 10); // Limit to top 10 suggestions } private async generatePatternSuggestions(patterns: ContextPattern[], context: AnalysisContext): Promise<ContextSuggestion[]> { const suggestions: ContextSuggestion[] = []; for (const pattern of patterns) { const suggestion: ContextSuggestion = { id: this.generateSuggestionId(), type: SuggestionType.REFACTOR, description: `Address ${pattern.type} pattern: ${pattern.description}`, priority: this.impactToPriority(pattern.impact), impact: { businessImpact: pattern.impact * 0.8, technicalImpact: pattern.impact, userImpact: pattern.impact * 0.6, priorityScore: pattern.impact * 0.8, effortEstimate: pattern.frequency * 0.5, riskLevel: RiskLevel.MEDIUM }, effort: pattern.frequency * 0.5, implementation: { steps: [ { step: 1, description: 'Analyze affected code areas', verification: 'Code analysis completed' }, { step: 2, description: 'Implement pattern fixes', verification: 'Pattern resolved' }, { step: 3, description: 'Add preventive measures', verification: 'Prevention in place' } ], resources: pattern.suggestions, risks: ['Regression potential', 'Performance impact'], benefits: ['Improved code quality', 'Reduced technical debt'] }, alternatives: [ { id: 'alt-1', description: 'Gradual refactoring approach', pros: ['Lower risk', 'Easier to manage'], cons: ['Takes longer', 'Partial benefits'], effort: pattern.frequency * 0.3, impact: { businessImpact: pattern.impact * 0.6, technicalImpact: pattern.impact * 0.7, userImpact: pattern.impact * 0.5, priorityScore: pattern.impact * 0.6, effortEstimate: pattern.frequency * 0.3, riskLevel: RiskLevel.LOW } } ], contextRelevance: 0.9, teamAlignment: this.calculateTeamAlignment(pattern, context), historicalSuccess: 0.75, implementationComplexity: pattern.impact * 0.2, expectedROI: pattern.impact * 1.5, contextualFactors: ['Pattern frequency', 'Team experience', 'Project stage'] }; suggestions.push(suggestion); } return suggestions; } private async generateCorrelationSuggestions(correlations: ContextCorrelation[], context: AnalysisContext): Promise<ContextSuggestion[]> { const suggestions: ContextSuggestion[] = []; for (const correlation of correlations) { const suggestion: ContextSuggestion = { id: this.generateSuggestionId(), type: SuggestionType.OPTIMIZE, description: `Address correlation: ${correlation.description}`, priority: this.impactToPriority(correlation.strength * 10), impact: { businessImpact: correlation.strength * 6, technicalImpact: correlation.strength * 8, userImpact: correlation.strength * 5, priorityScore: correlation.strength * 6.3, effortEstimate: correlation.relatedIssues.length * 0.3, riskLevel: RiskLevel.MEDIUM }, effort: correlation.relatedIssues.length * 0.3, implementation: { steps: [ { step: 1, description: 'Investigate correlation root causes', verification: 'Root cause identified' }, { step: 2, description: 'Implement systemic fixes', verification: 'System fixed' }, { step: 3, description: 'Monitor for recurrence', verification: 'Monitoring active' } ], resources: correlation.causalFactors, risks: ['System complexity', 'Unintended side effects'], benefits: ['Reduced issue recurrence', 'Improved system stability'] }, alternatives: [], contextRelevance: 0.85, teamAlignment: this.calculateTeamAlignment(correlation, context), historicalSuccess: 0.7, implementationComplexity: correlation.strength * 3, expectedROI: correlation.strength * 12, contextualFactors: ['Correlation strength', 'Causal factors', 'System complexity'] }; suggestions.push(suggestion); } return suggestions; } private async generateImpactSuggestions(impactAssessment: ContextImpactAssessment, context: AnalysisContext): Promise<ContextSuggestion[]> { const suggestions: ContextSuggestion[] = []; // Generate suggestions based on business impact if (impactAssessment.businessImpact.revenue < 7) { suggestions.push({ id: this.generateSuggestionId(), type: SuggestionType.OPTIMIZE, description: 'Improve revenue-generating features', priority: PriorityLevel.HIGH, impact: impactAssessment.overallImpact, effort: 5, implementation: { steps: [ { step: 1, description: 'Identify revenue-critical code', verification: 'Code identified' }, { step: 2, description: 'Optimize performance', verification: 'Performance improved' }, { step: 3, description: 'Enhance user experience', verification: 'UX enhanced' } ], resources: ['Performance profiling tools', 'User feedback'], risks: ['Feature disruption', 'Performance regression'], benefits: ['Increased revenue', 'Better user satisfaction'] }, alternatives: [], contextRelevance: 0.95, teamAlignment: 0.8, historicalSuccess: 0.8, implementationComplexity: 0.6, expectedROI: 15, contextualFactors: ['Revenue impact', 'Business criticality'] }); } // Generate suggestions based on technical impact if (impactAssessment.technicalImpact.maintainability < 6) { suggestions.push({ id: this.generateSuggestionId(), type: SuggestionType.REFACTOR, description: 'Improve code maintainability', priority: PriorityLevel.HIGH, impact: impactAssessment.overallImpact, effort: 4, implementation: { steps: [ { step: 1, description: 'Refactor complex code', verification: 'Code simplified' }, { step: 2, description: 'Improve documentation', verification: 'Documentation updated' }, { step: 3, description: 'Add unit tests', verification: 'Tests added' } ], resources: ['Refactoring tools', 'Documentation templates'], risks: ['Introduction of bugs', 'Time investment'], benefits: ['Easier maintenance', 'Faster development'] }, alternatives: [], contextRelevance: 0.9, teamAlignment: 0.85, historicalSuccess: 0.75, implementationComplexity: 0.5, expectedROI: 12, contextualFactors: ['Maintainability score', 'Team productivity'] }); } return suggestions; } private async generateTeamSuggestions(results: EnhancedAnalysisResult[], context: AnalysisContext): Promise<ContextSuggestion[]> { const suggestions: ContextSuggestion[] = []; // Generate suggestions based on team experience if (context.teamContext.experienceLevel === ExperienceLevel.JUNIOR) { suggestions.push({ id: this.generateSuggestionId(), type: SuggestionType.DOCUMENTATION, description: 'Improve documentation for junior developers', priority: PriorityLevel.HIGH, impact: { businessImpact: 6, technicalImpact: 7, userImpact: 8, priorityScore: 7, effortEstimate: 3, riskLevel: RiskLevel.LOW }, effort: 3, implementation: { steps: [ { step: 1, description: 'Create comprehensive documentation', verification: 'Documentation created' }, { step: 2, description: 'Add code examples', verification: 'Examples added' }, { step: 3, description: 'Conduct training sessions', verification: 'Training completed' } ], resources: ['Documentation tools', 'Training materials'], risks: ['Documentation maintenance', 'Time investment'], benefits: ['Faster onboarding', 'Reduced errors'] }, alternatives: [], contextRelevance: 0.95, teamAlignment: 0.9, historicalSuccess: 0.85, implementationComplexity: 0.4, expectedROI: 10, contextualFactors: ['Team experience level', 'Onboarding efficiency'] }); } return suggestions; } private async generateInsights( request: ContextAnalysisRequest, results: EnhancedAnalysisResult[], patterns: ContextPattern[], correlations: ContextCorrelation[], impactAssessment: ContextImpactAssessment ): Promise<ContextInsight[]> { const insights: ContextInsight[] = []; // Generate quality trend insights const qualityInsight = await this.generateQualityTrendInsight(results, request.context); if (qualityInsight) insights.push(qualityInsight); // Generate team pattern insights const teamInsight = await this.generateTeamPatternInsight(results, request.context); if (teamInsight) insights.push(teamInsight); // Generate technology risk insights const techInsight = await this.generateTechnologyRiskInsight(results, patterns, request.context); if (techInsight) insights.push(techInsight); // Generate business impact insights const businessInsight = await this.generateBusinessImpactInsight(impactAssessment, request.context); if (businessInsight) insights.push(businessInsight); // Generate process improvement insights const processInsight = await this.generateProcessImprovementInsight(results, correlations, request.context); if (processInsight) insights.push(processInsight); return insights; } private async generateQualityTrendInsight(results: EnhancedAnalysisResult[], context: AnalysisContext): Promise<ContextInsight | null> { const totalIssues = results.reduce((sum, r) => sum + r.issues.length, 0); const avgContextScore = results.reduce((sum, r) => sum + r.contextScore, 0) / results.length; if (totalIssues > 10 && avgContextScore < 70) { return { id: this.generateInsightId(), type: InsightType.QUALITY_TREND, title: 'Declining code quality detected', description: 'High number of issues with low context scores indicate declining code quality', confidence: 0.85, impact: { businessImpact: 8, technicalImpact: 9, userImpact: 7, priorityScore: 8, effortEstimate: 4, riskLevel: RiskLevel.HIGH }, evidence: [ `${totalIssues} total issues found`, `Average context score: ${avgContextScore.toFixed(1)}%`, 'Multiple files affected' ], recommendations: [ 'Implement code quality gates', 'Increase automated testing coverage', 'Conduct focused code reviews', 'Provide team training on best practices' ], context: { projectContext: context, teamContext: context.teamContext, historicalContext: context.historicalData, marketContext: { industry: 'Technology', competition: 'High', trends: ['Quality focus', 'DevOps adoption'], regulations: [] } } }; } return null; } private async generateTeamPatternInsight(results: EnhancedAnalysisResult[], context: AnalysisContext): Promise<ContextInsight | null> { const teamIssues = results.flatMap(r => r.issues).filter(issue => this.isTeamRelated(issue, context.teamContext) ); if (teamIssues.length > 5) { return { id: this.generateInsightId(), type: InsightType.TEAM_PATTERN, title: 'Team collaboration patterns identified', description: 'Multiple issues related to team collaboration and communication patterns', confidence: 0.8, impact: { businessImpact: 6, technicalImpact: 7, userImpact: 8, priorityScore: 7, effortEstimate: 3, riskLevel: RiskLevel.MEDIUM }, evidence: [ `${teamIssues.length} team-related issues`, `Collaboration style: ${context.teamContext.collaborationStyle}`, 'Multiple team members affected' ], recommendations: [ 'Improve communication protocols', 'Establish clear code ownership guidelines', 'Implement better documentation practices', 'Consider pair programming for complex areas' ], context: { projectContext: context, teamContext: context.teamContext, historicalContext: context.historicalData, marketContext: { industry: 'Technology', competition: 'Medium', trends: ['Agile methodologies', 'Remote work'], regulations: [] } } }; } return null; } private async generateTechnologyRiskInsight(results: EnhancedAnalysisResult[], patterns: ContextPattern[], context: AnalysisContext): Promise<ContextInsight | null> { const techPatterns = patterns.filter(p => p.context.technologyFactors.length > 0 && p.impact > 7 ); if (techPatterns.length > 0) { return { id: this.generateInsightId(), type: InsightType.TECHNOLOGY_RISK, title: 'Technology-specific risks identified', description: 'High-impact patterns related to specific technologies in the stack', confidence: 0.9, impact: { businessImpact: 8, technicalImpact: 9, userImpact: 7, priorityScore: 8, effortEstimate: 5, riskLevel: RiskLevel.HIGH }, evidence: [ `${techPatterns.length} high-impact technology patterns`, `Technologies involved: ${techPatterns.map(p => p.context.technologyFactors.join(', ')).join(', ')}`, 'Multiple system components affected' ], recommendations: [ 'Review technology stack choices', 'Implement technology-specific best practices', 'Consider alternative technologies for high-risk areas', 'Invest in team training for problematic technologies' ], context: { projectContext: context, teamContext: context.teamContext, historicalContext: context.historicalData, marketContext: { industry: 'Technology', competition: 'High', trends: ['Technology evolution', 'Cloud migration'], regulations: [] } } }; } return null; } private async generateBusinessImpactInsight(impactAssessment: ContextImpactAssessment, context: AnalysisContext): Promise<ContextInsight | null> { if (impactAssessment.businessImpact.revenue < 6 || impactAssessment.businessImpact.risk > 7) { return { id: this.generateInsightId(), type: InsightType.BUSINESS_IMPACT, title: 'Significant business impact identified', description: 'Current issues pose significant risk to business objectives and revenue', confidence: 0.85, impact: impactAssessment.overallImpact, evidence: [ `Revenue impact score: ${impactAssessment.businessImpact.revenue.toFixed(1)}`, `Risk level: ${impactAssessment.businessImpact.risk.toFixed(1)}`, `Cost impact: ${impactAssessment.businessImpact.cost.toFixed(1)}` ], recommendations: [ 'Prioritize revenue-critical fixes', 'Implement risk mitigation strategies', 'Consider business process improvements', 'Align technical debt reduction with business priorities' ], context: { projectContext: context, teamContext: context.teamContext, historicalContext: context.historicalData, marketContext: { industry: 'Technology', competition: 'High', trends: ['Digital transformation', 'Customer experience'], regulations: [] } } }; } return null; } private async generateProcessImprovementInsight(results: EnhancedAnalysisResult[], correlations: ContextCorrelation[], context: AnalysisContext): Promise<ContextInsight | null> { const processCorrelations = correlations.filter(c => c.causalFactors.some(factor => factor.includes('process') || factor.includes('workflow') || factor.includes('methodology') ) ); if (processCorrelations.length > 0) { return { id: this.generateInsightId(), type: InsightType.PROCESS_IMPROVEMENT, title: 'Process improvement opportunities identified', description: 'Correlations indicate opportunities for development process improvements', confidence: 0.8, impact: { businessImpact: 7, technicalImpact: 6, userImpact: 8, priorityScore: 7, effortEstimate: 3, riskLevel: RiskLevel.MEDIUM }, evidence: [ `${processCorrelations.length} process-related correlations`, `Causal factors: ${processCorrelations.map(c => c.causalFactors.join(', ')).join(', ')}`, 'Multiple development phases affected' ], recommendations: [ 'Review and optimize development workflows', 'Implement better quality gates', 'Improve communication between team members', 'Consider adopting or refining agile methodologies' ], context: { projectContext: context, teamContext: context.teamContext, historicalContext: context.historicalData, marketContext: { industry: 'Technology', competition: 'Medium', trends: ['Agile transformation', 'DevOps adoption'], regulations: [] } } }; } return null; } private async createAnalysisMetadata(request: ContextAnalysisRequest, generationTime: number): Promise<AnalysisMetadata> { return { generatedAt: new Date(), generationTime, analysisDepth: request.options.depth, patternsFound: 0, // Will be calculated by caller correlationsFound: 0, // Will be calculated by caller suggestionsGenerated: 0, // Will be calculated by caller insightsGenerated: 0, // Will be calculated by caller contextFactors: [] }; } private createDefaultImpactAssessment(): ContextImpactAssessment { return { overallImpact: { businessImpact: 5, technicalImpact: 5, userImpact: 5, priorityScore: 5, effortEstimate: 5, riskLevel: RiskLevel.MEDIUM }, categoryImpacts: [], teamImpact: { productivity: 5, morale: 5, learning: 5, collaboration: 5 }, businessImpact: { revenue: 5, cost: 5, risk: 5, opportunity: 5 }, technicalImpact: { maintainability: 5, scalability: 5, performance: 5, security: 5 } }; } // Helper methods private getProjectTypeRelevance(result: UnifiedAnalysisResult, projectType: ProjectType): number { const relevanceMap = { [ProjectType.WEB_APP]: { [IssueCategory.PERFORMANCE]: 0.9, [IssueCategory.SECURITY]: 0.8 }, [ProjectType.MOBILE_APP]: { [IssueCategory.PERFORMANCE]: 0.95, [IssueCategory.ACCESSIBILITY]: 0.9 }, [ProjectType.API]: { [IssueCategory.SECURITY]: 0.95, [IssueCategory.PERFORMANCE]: 0.85 }, [ProjectType.LIBRARY]: { [IssueCategory.MAINTAINABILITY]: 0.9, [IssueCategory.DOCUMENTATION]: 0.85 }, [ProjectType.MICROSERVICE]: { [IssueCategory.SECURITY]: 0.9, [IssueCategory.PERFORMANCE]: 0.85 }, [ProjectType.MONOLITH]: { [IssueCategory.MAINTAINABILITY]: 0.85, [IssueCategory.SCALABILITY]: 0.8 } }; const typeRelevance = relevanceMap[projectType] || {}; const issues = result.issues; if (issues.length === 0) return 0.5; const avgRelevance = issues.reduce((sum, issue) => { return sum + (typeRelevance[issue.category] || 0.5); }, 0) / issues.length; return avgRelevance; } private getDevelopmentStageRelevance(result: UnifiedAnalysisResult, stage: DevelopmentStage): number { const stageMultipliers = { [DevelopmentStage.PLANNING]: { [SeverityLevel.ERROR]: 1.2, [SeverityLevel.WARNING]: 1.1 }, [DevelopmentStage.DEVELOPMENT]: { [SeverityLevel.ERROR]: 1.0, [SeverityLevel.WARNING]: 1.0 }, [DevelopmentStage.TESTING]: { [SeverityLevel.ERROR]: 1.5, [SeverityLevel.WARNING]: 1.3 }, [DevelopmentStage.DEPLOYMENT]: { [SeverityLevel.ERROR]: 2.0, [SeverityLevel.WARNING]: 1.8 }, [DevelopmentStage.MAINTENANCE]: { [SeverityLevel.ERROR]: 1.3, [SeverityLevel.WARNING]: 1.2 } }; const multiplier = stageMultipliers[stage] || { [SeverityLevel.ERROR]: 1.0, [SeverityLevel.WARNING]: 1.0 }; const issues = result.issues; if (issues.length === 0) return 0.5; const weightedScore = issues.reduce((sum, issue) => { return sum + (multiplier[issue.severity] || 1.0) * this.severityToWeight(issue.severity); }, 0) / issues.length; return Math.min(1.0, weightedScore / 10); } private getTeamExperienceRelevance(result: UnifiedAnalysisResult, experience: ExperienceLevel): number { const experienceMultipliers = { [ExperienceLevel.JUNIOR]: { [IssueCategory.SYNTAX]: 1.5, [IssueCategory.STYLE]: 1.3 }, [ExperienceLevel.MID]: { [IssueCategory.SYNTAX]: 1.0, [IssueCategory.STYLE]: 1.0 }, [ExperienceLevel.SENIOR]: { [IssueCategory.SYNTAX]: 0.7, [IssueCategory.STYLE]: 0.8 }, [ExperienceLevel.EXPERT]: { [IssueCategory.SYNTAX]: 0.5, [IssueCategory.STYLE]: 0.6 } }; const multiplier = experienceMultipliers[experience] || { [IssueCategory.SYNTAX]: 1.0, [IssueCategory.STYLE]: 1.0 }; const issues = result.issues; if (issues.length === 0) return 0.5; const weightedScore = issues.reduce((sum, issue) => { return sum + (multiplier[issue.category] || 1.0) * this.severityToWeight(issue.severity); }, 0) / issues.length; return Math.min(1.0, weightedScore / 10); } private getHistoricalPerformanceRelevance(result: UnifiedAnalysisResult, historicalData: HistoricalData): number { // This would analyze historical performance data and adjust relevance // For now, return a default value return 0.7; } private getUserPreferencesRelevance(result: UnifiedAnalysisResult, preferences: UserPreferences): number { const issues = result.issues; if (issues.length === 0) return 0.5; // Check if issues match user's preferred categories const preferredIssues = issues.filter(issue => preferences.preferredCategories.includes(issue.category) ); const preferredRatio = preferredIssues.length / issues.length; // Adjust based on severity threshold const severeIssues = issues.filter(issue => this.severityToWeight(issue.severity) >= this.severityToWeight(preferences.severityThreshold) ); const severeRatio = severeIssues.length / issues.length; return (preferredRatio * 0.6) + (severeRatio * 0.4); } private getCategoryRelevance(issues: UnifiedIssue[], context: AnalysisContext): number { // Calculate relevance based on project priorities and issue categories const categoryCounts = new Map<IssueCategory, number>(); for (const issue of issues) { categoryCounts.set(issue.category, (categoryCounts.get(issue.category) || 0) + 1); } // This would be more sophisticated in practice return Math.random() * 30 + 10; // 10-40 range } private getSeverityRelevance(issues: UnifiedIssue[], stage: DevelopmentStage): number { // Calculate relevance based on severity and development stage const severityWeights = issues.reduce((sum, issue) => { return sum + this.severityToWeight(issue.severity); }, 0); const avgSeverity = severityWeights / issues.length; // Adjust based on development stage const stageMultiplier = { [DevelopmentStage.PLANNING]: 0.8, [DevelopmentStage.DEVELOPMENT]: 1.0, [DevelopmentStage.TESTING]: 1.5, [DevelopmentStage.DEPLOYMENT]: 2.0, [DevelopmentStage.MAINTENANCE]: 1.2 }; return avgSeverity * (stageMultiplier[stage] || 1.0) * 5; } private getTechnologyRelevance(result: UnifiedAnalysisResult, technologies: string[]): number { // Calculate relevance based on technology stack alignment // This would be more sophisticated in practice return Math.random() * 20 + 15; // 15-35 range } private getTeamRelevance(result: UnifiedAnalysisResult, teamContext: TeamContext): number { // Calculate relevance based on team capabilities and collaboration style // This would be more sophisticated in practice return Math.random() * 20 + 15; // 15-35 range } private severityToWeight(severity: SeverityLevel): number { const weights = { [SeverityLevel.ERROR]: 3, [SeverityLevel.WARNING]: 2, [SeverityLevel.INFO]: 1, [SeverityLevel.HINT]: 0.5 }; return weights[severity] || 1; } private impactToPriority(impact: number): PriorityLevel { if (impact >= 8) return PriorityLevel.CRITICAL; if (impact >= 6) return PriorityLevel.HIGH; if (impact >= 4) return PriorityLevel.MEDIUM; if (impact >= 2) return PriorityLevel.LOW; return PriorityLevel.INFO; } private calculateRiskLevel(criticalIssues: number, totalIssues: number): RiskLevel { const criticalRatio = criticalIssues / totalIssues; if (criticalRatio >= 0.3) return RiskLevel.CRITICAL; if (criticalRatio >= 0.2) return RiskLevel.HIGH; if (criticalRatio >= 0.1) return RiskLevel.MEDIUM; return RiskLevel.LOW; } private calculateTrendImpact(trend: QualityTrend): number { const impactMap = { [TrendDirection.IMPROVING]: -1, [TrendDirection.STABLE]: 0, [TrendDirection.DECLINING]: 1 }; return Math.abs(trend.value - trend.baseline) * impactMap[trend.trend]; } private groupIssuesByCategory(issues: UnifiedIssue[]): Map<IssueCategory, UnifiedIssue[]> { const groups = new Map<IssueCategory, UnifiedIssue[]>(); for (const issue of issues) { const categoryIssues = groups.get(issue.category) || []; categoryIssues.push(issue); groups.set(issue.category, categoryIssues); } return groups; } private isTechnologyRelated(issue: UnifiedIssue, technology: string): boolean { // This would check if an issue is related to a specific technology // For now, return a random value return Math.random() > 0.7; } private isCollaborationRelated(issue: UnifiedIssue, style: CollaborationStyle): boolean { // This would check if an issue is related to collaboration style // For now, return a random value return Math.random() > 0.6; } private isTeamRelated(issue: UnifiedIssue, teamContext: TeamContext): boolean { // This would check if an issue is related to team factors // For now, return a random value return Math.random() > 0.5; } private calculatePatternConfidence(issues: UnifiedIssue[]): number { // Calculate confidence based on pattern consistency and strength const baseConfidence = Math.min(0.95, 0.5 + (issues.length * 0.1)); // Adjust based on severity consistency const severities = issues.map(i => i.severity); const severityVariance = this.calculateVariance(severities.map(s => this.severityToWeight(s))); const severityConsistency = Math.max(0, 1 - (severityVariance / 4)); return baseConfidence * severityConsistency; } private calculatePatternImpact(issues: UnifiedIssue[]): number { // Calculate impact based on issue severity and frequency const severitySum = issues.reduce((sum, issue) => sum + this.severityToWeight(issue.severity), 0); return Math.min(10, severitySum / issues.length * 2); } private calculateCorrelationConfidence(issues: UnifiedIssue[]): number { // Calculate confidence for correlation const baseConfidence = Math.min(0.9, 0.4 + (issues.length * 0.15)); // Adjust based on spatial/temporal proximity // This would be more sophisticated in practice return baseConfidence; } private groupIssuesByTimePattern(issues: UnifiedIssue[]): Map<string, UnifiedIssue[]> { // Group issues by time patterns (e.g., time of day, day of week) // This would be more sophisticated in practice const groups = new Map<string, UnifiedIssue[]>(); // Simple grouping by issue category for now for (const issue of issues) { const key = issue.category; const categoryIssues = groups.get(key) || []; categoryIssues.push(issue); groups.set(key, categoryIssues); } return groups; } private groupIssuesByFile(issues: UnifiedIssue[]): Map<string, UnifiedIssue[]> { const groups = new Map<string, UnifiedIssue[]>(); for (const issue of issues) { const filePath = issue.location.filePath; const fileIssues = groups.get(filePath) || []; fileIssues.push(issue); groups.set(filePath, fileIssues); } return groups; } private groupIssuesByTechnology(issues: UnifiedIssue[], technologies: string[]): Map<string, UnifiedIssue[]> { const groups = new Map<string, UnifiedIssue[]>(); for (const issue of issues) { // Find matching technology (simplified) const matchingTech = technologies.find(tech => this.isTechnologyRelated(issue, tech)); if (matchingTech) { const techIssues = groups.get(matchingTech) || []; techIssues.push(issue); groups.set(matchingTech, techIssues); } } return groups; } private extractModules(filePath: string): string[] { // Extract module names from file path // This would be more sophisticated in practice return [filePath.split('/')[1] || 'root']; } private extractComponents(filePath: string): string[] { // Extract component names from file path // This would be more sophisticated in practice return [filePath.split('/').pop()?.split('.')[0] || 'component']; } private calculateVariance(values: number[]): number { const mean = values.reduce((sum, val) => sum + val, 0) / values.length; const variance = values.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / values.length; return variance; } private calculateTeamAlignment(pattern: ContextPattern | ContextCorrelation, context: AnalysisContext): number { // Calculate how well the pattern/correlation aligns with team capabilities const experienceMultiplier = { [ExperienceLevel.JUNIOR]: 0.7, [ExperienceLevel.MID]: 0.85, [ExperienceLevel.SENIOR]: 0.95, [ExperienceLevel.EXPERT]: 1.0 }; return experienceMultiplier[context.teamContext.experienceLevel] || 0.8; } // ID generation methods private generateAnalysisId(): string { return `analysis_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`; } private generatePatternId(): string { return `pattern_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`; } private generateCorrelationId(): string { return `correlation_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`; } private generateSuggestionId(): string { return `suggestion_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`; } private generateInsightId(): string { return `insight_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`; } private async loadDatabases(): Promise<void> { try { // Load pattern database const cachedPatterns = await this.cache.get<Map<string, ContextPattern[]>>('pattern-database'); if (cachedPatterns) { this.patternDatabase = new Map(Object.entries(cachedPatterns)); } // Load correlation database const cachedCorrelations = await this.cache.get<Map<string, ContextCorrelation[]>>('correlation-database'); if (cachedCorrelations) { this.correlationDatabase = new Map(Object.entries(cachedCorrelations)); } this.logger.info('Pattern and correlation databases loaded'); } catch (error) { this.logger.warn('Failed to load databases:', error); } } private async loadLearningData(): Promise<void> { try { const cachedLearning = await this.cache.get<Map<string, LearningData>>('learning-data'); if (cachedLearning) { this.learningData = new Map(Object.entries(cachedLearning)); } this.logger.info('Learning data loaded'); } catch (error) { this.logger.warn('Failed to load learning data:', error); } } private async updateLearningData(request: ContextAnalysisRequest, result: ContextAnalysisResult): Promise<void> { if (!this.config.enableLearning) return; try { // Update learning data based on analysis results const learningData: LearningData = { projectId: request.projectId, lastAnalysis: new Date(), patternsDiscovered: result.patterns.length, correlationsFound: result.correlations.length, suggestionsGenerated: result.suggestions.length, insightsGenerated: result.insights.length, contextFactors: result.metadata.contextFactors, effectiveness: this.calculateAnalysisEffectiveness(result) }; this.learningData.set(request.projectId, learningData); // Save to cache await this.cache.set('learning-data', Object.fromEntries(this.learningData), 3600); this.logger.info('Learning data updated'); } catch (error) { this.logger.warn('Failed to update learning data:', error); } } private calculateAnalysisEffectiveness(result: ContextAnalysisResult): number { // Calculate effectiveness based on various factors const patternEffectiveness = result.patterns.length > 0 ? 0.3 : 0; const correlationEffectiveness = result.correlations.length > 0 ? 0.3 : 0; const suggestionEffectiveness = result.suggestions.length > 0 ? 0.2 : 0; const insightEffectiveness = result.insights.length > 0 ? 0.2 : 0; return patternEffectiveness + correlationEffectiveness + suggestionEffectiveness + insightEffectiveness; } // Event handlers private handlePatternDiscovered(pattern: ContextPattern): void { this.logger.debug(`Pattern discovered event handled: ${pattern.description}`); } private handleCorrelationFound(correlation: ContextCorrelation): void { this.logger.debug(`Correlation found event handled: ${correlation.description}`); } private handleInsightGenerated(insight: ContextInsight): void { this.logger.debug(`Insight generated event handled: ${insight.title}`); } private handleContextUpdated(context: AnalysisContext): void { this.logger.debug('Context updated event handled'); } // Public API methods async updateConfig(config: Partial<ContextAnalysisConfig>): Promise<void> { this.config = { ...this.config, ...config }; this.logger.info('Context analysis configuration updated'); } async getAnalysisStats(): Promise<any> { return { totalPatterns: Array.from(this.patternDatabase.values()).reduce((sum, patterns) => sum + patterns.length, 0), totalCorrelations: Array.from(this.correlationDatabase.values()).reduce((sum, correlations) => sum + correlations.length, 0), learningEnabled: this.config.enableLearning, patternThreshold: this.config.patternThreshold, correlationThreshold: this.config.correlationThreshold, projectsAnalyzed: this.learningData.size }; } async shutdown(): Promise<void> { this.logger.info('Shutting down Context-Aware Analysis Engine'); // Save databases to cache try { await this.cache.set('pattern-database', Object.fromEntries(this.patternDatabase), 3600); await this.cache.set('correlation-database', Object.fromEntries(this.correlationDatabase), 3600); await this.cache.set('learning-data', Object.fromEntries(this.learningData), 3600); } catch (error) { this.logger.warn('Failed to save databases during shutdown:', error); } // Clear event listeners this.removeAllListeners(); this.logger.info('Context-Aware Analysis Engine shutdown complete'); } } interface LearningData { projectId: string; lastAnalysis: Date; patternsDiscovered: number; correlationsFound: number; suggestionsGenerated: number; insightsGenerated: number; contextFactors: ContextualFactor[]; effectiveness: number; }