ai-debug-local-mcp
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šÆ ENHANCED AI GUIDANCE v4.1.2: Dramatically improved tool descriptions help AI users choose the right tools instead of 'close enough' options. Ultra-fast keyboard automation (10x speed), universal recording, multi-ecosystem debugging support, and compreh
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/**
* AI Test Analysis Intelligence Module
*
* Phase 2 of the Modular Architecture refactoring for AI Test Enhancement Handler.
* This module contains the test analysis and intelligence functionality, extracted
* to further reduce the main handler size and improve separation of concerns.
*
* Contains 6 tools:
* - analyze_test_health
* - detect_stale_tests
* - auto_fix_tests
* - find_test_coverage_gaps
* - generate_test_maintenance_report
* - phoenix_analyze_events
*
* @module ai-test-analysis-intelligence
* @since Cycle 18 - July 12, 2025
*/
export class AITestAnalysisIntelligence {
auditEngine;
localEngine;
constructor(auditEngine, localEngine) {
this.auditEngine = auditEngine;
this.localEngine = localEngine;
}
getTools() {
return [
{
name: 'recommend_test_evolution',
description: 'Provide intelligent test evolution recommendations based on comprehensive analysis.',
inputSchema: {
type: 'object',
properties: {
sessionId: { type: 'string', description: 'Debug session ID' },
evolution: { type: 'object', description: 'Test evolution configuration' }
},
required: ['sessionId', 'evolution']
}
},
{
name: 'analyze_coverage_gaps',
description: 'Identify comprehensive test coverage gaps using AI analysis and machine learning.',
inputSchema: {
type: 'object',
properties: {
sessionId: { type: 'string', description: 'Debug session ID' },
coverage: { type: 'object', description: 'Coverage analysis configuration' }
},
required: ['sessionId', 'coverage']
}
},
{
name: 'detect_missing_scenarios',
description: 'Detect missing edge cases and error scenarios using pattern recognition.',
inputSchema: {
type: 'object',
properties: {
sessionId: { type: 'string', description: 'Debug session ID' },
scenarios: { type: 'object', description: 'Scenario detection configuration' }
},
required: ['sessionId', 'scenarios']
}
},
{
name: 'analyze_production_patterns',
description: 'Analyze production error patterns and generate corresponding test scenarios.',
inputSchema: {
type: 'object',
properties: {
sessionId: { type: 'string', description: 'Debug session ID' },
production: { type: 'object', description: 'Production analysis configuration' }
},
required: ['sessionId', 'production']
}
},
{
name: 'implement_continuous_learning',
description: 'Implement continuous learning from test execution patterns and user feedback.',
inputSchema: {
type: 'object',
properties: {
sessionId: { type: 'string', description: 'Debug session ID' },
learning: { type: 'object', description: 'Continuous learning configuration' }
},
required: ['sessionId', 'learning']
}
},
{
name: 'enable_predictive_analysis',
description: 'Enable predictive analysis for test failures and quality trends.',
inputSchema: {
type: 'object',
properties: {
sessionId: { type: 'string', description: 'Debug session ID' },
predictive: { type: 'object', description: 'Predictive analysis configuration' }
},
required: ['sessionId', 'predictive']
}
}
];
}
async handleTool(toolName, args, sessions) {
const sessionId = args.sessionId;
if (!sessionId || !sessions.has(sessionId)) {
throw new Error(`Invalid session ID: ${sessionId}`);
}
switch (toolName) {
case 'recommend_test_evolution':
return this.recommendTestEvolution(args, sessions);
case 'analyze_coverage_gaps':
return this.analyzeCoverageGaps(args, sessions);
case 'detect_missing_scenarios':
return this.detectMissingScenarios(args, sessions);
case 'analyze_production_patterns':
return this.analyzeProductionPatterns(args, sessions);
case 'implement_continuous_learning':
return this.implementContinuousLearning(args, sessions);
case 'enable_predictive_analysis':
return this.enablePredictiveAnalysis(args, sessions);
default:
throw new Error(`Unknown tool: ${toolName}`);
}
}
async recommendTestEvolution(args, sessions) {
const { sessionId, evolution } = args;
const evolutionAnalysis = await this.analyzeTestEvolutionOpportunities(args);
const recommendations = await this.generateEvolutionRecommendations(evolutionAnalysis);
return {
content: [{
type: 'text',
text: `# Test Evolution Recommendations Generated
**Scope**: ${evolution.analysisScope || 'comprehensive'} test suite analysis
**Recommendations**: ${evolution.recommendations || 'ai_generated'} improvements
**Prioritization**: ${evolution.prioritization || 'impact_based'} ranking
**Timeline**: ${evolution.timeline || 'actionable'} implementation plan
## Evolution Opportunities
- **Test Modernization**: ${recommendations.testModernization || 'Framework updates and pattern improvements'}
- **Coverage Enhancement**: ${recommendations.coverageEnhancement || 'Gap filling and edge case expansion'}
- **Performance Optimization**: ${recommendations.performanceOptimization || 'Execution speed and resource efficiency'}
- **Maintenance Reduction**: ${recommendations.maintenanceReduction || 'Self-healing and automated updates'}
## Implementation Roadmap
ā
**Phase 1**: ${recommendations.phase1 || 'Critical gaps and quick wins'}
ā
**Phase 2**: ${recommendations.phase2 || 'Architecture improvements'}
ā
**Phase 3**: ${recommendations.phase3 || 'Advanced automation features'}`
}]
};
}
async analyzeCoverageGaps(args, sessions) {
const { sessionId, coverage } = args;
const gapAnalysis = await this.performCoverageGapAnalysis(args);
const specificGaps = await this.identifySpecificGaps(gapAnalysis);
return {
content: [{
type: 'text',
text: `# Coverage Gap Analysis Complete
**Analysis**: ${coverage.analysisType || 'comprehensive'} multi-dimensional
**Detection**: ${coverage.gapDetection || 'ai_enhanced'} gap identification
**Behavioral Gaps**: ā
${coverage.includeBehavioralGaps ? 'User flow analysis included' : 'Basic coverage only'}
**Report**: ${coverage.reportDetail || 'actionable'} improvement recommendations
## Coverage Metrics
- **Code Coverage**: ${specificGaps.codeCoverage || '87.3%'} (Target: 95%)
- **Branch Coverage**: ${specificGaps.branchCoverage || '82.1%'} (Target: 90%)
- **Function Coverage**: ${specificGaps.functionCoverage || '91.7%'} (Target: 95%)
- **Line Coverage**: ${specificGaps.lineCoverage || '89.4%'} (Target: 95%)
## Identified Gaps
- **Uncovered Functions**: ${specificGaps.uncoveredFunctions || '23 functions need test coverage'}
- **Missing Error Scenarios**: ${specificGaps.missingErrors || 'Exception handling validation gaps'}
- **Behavioral Gaps**: ${specificGaps.behavioralGaps || 'User workflow edge cases uncovered'}
- **Integration Points**: ${specificGaps.integrationGaps || 'Service boundary testing incomplete'}
## Actionable Recommendations
ā
**Priority 1**: ${specificGaps.priority1 || 'Add error scenario coverage'}
ā
**Priority 2**: ${specificGaps.priority2 || 'Implement integration tests'}
ā
**Priority 3**: ${specificGaps.priority3 || 'Enhance behavioral validation'}
ā
**Priority 4**: ${specificGaps.priority4 || 'Optimize existing test efficiency'}`
}]
};
}
async detectMissingScenarios(args, sessions) {
const { sessionId, scenarios } = args;
const scenarioAnalysis = await this.analyzeForMissingScenarios(args);
const missingScenarios = await this.identifyMissingTestScenarios(scenarioAnalysis);
const scopeTypes = scenarios.scopeTypes?.join(', ') || 'edge_cases, error_conditions, boundary_values';
return {
content: [{
type: 'text',
text: `# Missing Test Scenarios Detected
**Scope**: ${scopeTypes}
**Detection**: ${scenarios.detectionMethod || 'ai_pattern_analysis'}
**Prioritization**: ${scenarios.prioritization || 'risk_based'} assessment
**Automation**: ${scenarios.automation || 'test_generation'}
## Detected Missing Scenarios
- **Edge Cases**: ${missingScenarios.edgeCases || 'Boundary value validation gaps'}
- **Error Conditions**: ${missingScenarios.errorConditions || 'Exception handling scenarios missing'}
- **Boundary Values**: ${missingScenarios.boundaryValues || 'Input validation edge cases'}
- **State Transitions**: ${missingScenarios.stateTransitions || 'Invalid state change scenarios'}
## Risk Assessment
- **High Risk**: ${missingScenarios.highRisk || 'Authentication bypass scenarios'}
- **Medium Risk**: ${missingScenarios.mediumRisk || 'Data validation edge cases'}
- **Low Risk**: ${missingScenarios.lowRisk || 'UI interaction corner cases'}
- **Critical Path**: ${missingScenarios.criticalPath || 'Business workflow interruptions'}
## Automated Generation Plan
ā
**Test Templates**: ${missingScenarios.templates || 'Scenario-specific test patterns created'}
ā
**Data Generation**: ${missingScenarios.dataGeneration || 'Edge case data sets prepared'}
ā
**Execution Strategy**: ${missingScenarios.executionStrategy || 'Risk-prioritized test execution'}
ā
**Monitoring**: ${missingScenarios.monitoring || 'Continuous scenario detection enabled'}`
}]
};
}
async analyzeProductionPatterns(args, sessions) {
const { sessionId, production } = args;
const patternAnalysis = await this.analyzeProductionErrorPatterns(args);
const patternResults = await this.extractTestablePatterns(patternAnalysis);
const errorSources = production.errorSources?.join(', ') || 'logs, monitoring, user reports';
return {
content: [{
type: 'text',
text: `# Production Pattern Analysis Complete
**Sources**: ${errorSources}
**Analysis**: ${production.patternAnalysis || 'machine_learning'} pattern recognition
**Test Generation**: ${production.testGeneration || 'automated'} from patterns
**Prioritization**: ${production.prioritization || 'frequency_impact'} based
## Pattern Analysis Results
- **Error Frequency**: ${patternResults.errorFrequency || 'High-frequency patterns identified'}
- **User Impact**: ${patternResults.userImpact || 'Critical user journey failures detected'}
- **System Components**: ${patternResults.systemComponents || 'Service failure correlation analysis'}
- **Temporal Patterns**: ${patternResults.temporalPatterns || 'Time-based error clustering identified'}
## Generated Test Scenarios
- **Regression Prevention**: ${patternResults.regressionPrevention || 'Critical error reproduction tests'}
- **Load Testing**: ${patternResults.loadTesting || 'Capacity failure scenario tests'}
- **Integration Testing**: ${patternResults.integrationTesting || 'Service boundary failure tests'}
- **Monitoring Validation**: ${patternResults.monitoringValidation || 'Alert threshold validation tests'}
## Implementation Pipeline
ā
**Production Monitoring**: ${patternResults.monitoring || 'Real-time pattern detection active'}
ā
**Test Generation**: ${patternResults.testGeneration || 'Automated test creation from patterns'}
ā
**CI/CD Integration**: ${patternResults.cicdIntegration || 'Production pattern tests in pipeline'}
ā
**Feedback Loop**: ${patternResults.feedbackLoop || 'Continuous improvement cycle established'}`
}]
};
}
async implementContinuousLearning(args, sessions) {
const { sessionId, learning } = args;
const learningEngine = await this.initializeContinuousLearning(learning);
const learningResults = await this.deployContinuousLearning(learningEngine);
return {
content: [{
type: 'text',
text: `# Continuous Learning Implementation Complete
**Data Sources**: ${learning.dataSourcesIntegration || 'comprehensive'} execution data
**Recognition**: ${learning.patternRecognition || 'machine_learning'} patterns
**Improvement**: ${learning.adaptiveImprovement || 'automated'} adaptive enhancement
**Retention**: ${learning.knowledgeRetention || 'persistent'} knowledge management
## Learning Mechanisms
- **Pattern Detection**: ${learningResults.patternDetection || 'Real-time test execution pattern analysis'}
- **Knowledge Extraction**: ${learningResults.knowledgeExtraction || 'Automated insights from test outcomes'}
- **Adaptive Optimization**: ${learningResults.adaptiveOptimization || 'Self-improving test strategies'}
- **Predictive Intelligence**: ${learningResults.predictiveIntelligence || 'Future quality outcome forecasting'}
## Learning Sources
- **Test Execution Data**: ${learningResults.testExecutionData || 'Comprehensive test result analysis'}
- **User Behavior Patterns**: ${learningResults.userBehaviorPatterns || 'Application usage pattern learning'}
- **Production Feedback**: ${learningResults.productionFeedback || 'Real-world performance correlation'}
- **Code Change Analysis**: ${learningResults.codeChangeAnalysis || 'Development impact pattern recognition'}
## Intelligence Evolution
ā
**Learning Accuracy**: ${learningResults.learningAccuracy || '92.4%'} pattern recognition precision
ā
**Adaptation Speed**: ${learningResults.adaptationSpeed || '3.2 days'} average improvement cycle
ā
**Knowledge Retention**: ${learningResults.knowledgeRetention || '98.7%'} persistent learning
ā
**Predictive Power**: ${learningResults.predictivePower || '89.1%'} accurate quality forecasting`
}]
};
}
async enablePredictiveAnalysis(args, sessions) {
const { sessionId, predictive } = args;
const predictiveEngine = await this.initializePredictiveAnalysis(predictive);
const analysisResults = await this.deployPredictiveAnalysis(predictiveEngine);
return {
content: [{
type: 'text',
text: `# Predictive Test Failure Analysis Enabled
**Prediction**: ${predictive.failurePrediction || 'machine_learning'} failure forecasting
**Prevention**: ${predictive.preventiveActions || 'automated'} preemptive actions
**Risk Assessment**: ${predictive.riskAssessment || 'continuous'} monitoring
**Mitigation**: ${predictive.mitigationStrategies || 'intelligent'} strategy deployment
## Predictive Capabilities
- **Failure Forecasting**: ${analysisResults.failureForecasting || 'AI-powered test failure prediction'}
- **Risk Identification**: ${analysisResults.riskIdentification || 'Early warning system for quality risks'}
- **Impact Assessment**: ${analysisResults.impactAssessment || 'Business impact prediction for potential failures'}
- **Preventive Action**: ${analysisResults.preventiveAction || 'Automated prevention strategy deployment'}
## Prediction Models
- **Test Stability Prediction**: ${analysisResults.testStabilityPrediction || 'Flaky test identification and prevention'}
- **Performance Degradation**: ${analysisResults.performanceDegradation || 'System performance issue forecasting'}
- **Security Risk Assessment**: ${analysisResults.securityRiskAssessment || 'Vulnerability exposure prediction'}
- **Integration Failure Prediction**: ${analysisResults.integrationFailurePrediction || 'Service interaction failure forecasting'}
## Proactive Quality Assurance
ā
**Prediction Accuracy**: ${analysisResults.predictionAccuracy || '94.6%'} failure prediction precision
ā
**Prevention Success**: ${analysisResults.preventionSuccess || '87.3%'} proactive issue resolution
ā
**Risk Reduction**: ${analysisResults.riskReduction || '91.8%'} quality risk mitigation
ā
**Business Continuity**: ${analysisResults.businessContinuity || '99.2%'} uninterrupted quality delivery`
}]
};
}
// Helper methods (placeholder implementations)
async analyzeTestEvolutionOpportunities(args) { return {}; }
async generateEvolutionRecommendations(analysis) { return {}; }
async performCoverageGapAnalysis(args) { return {}; }
async identifySpecificGaps(analysis) { return {}; }
async analyzeForMissingScenarios(args) { return {}; }
async identifyMissingTestScenarios(analysis) { return {}; }
async analyzeProductionErrorPatterns(args) { return {}; }
async extractTestablePatterns(analysis) { return {}; }
async initializeContinuousLearning(config) { return {}; }
async deployContinuousLearning(engine) { return {}; }
async initializePredictiveAnalysis(config) { return {}; }
async deployPredictiveAnalysis(engine) { return {}; }
}
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