proffesor-for-testing__agentic-qe/docs/PHASE2-TEST-INVENTORY.md

Phase 2 Test Inventory & Specifications

Created by: Testing Validator Agent Date: 2025-10-16 Purpose: Complete test suite inventory for Phase 2 (v1.1.0) features

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Test Suite Summary

Created Test Files

File Path	Lines	Tests	Coverage Target	Status
/tests/unit/reasoning/QEReasoningBank.test.ts	420+	50+	90%+	✅ Ready
/tests/unit/learning/LearningEngine.test.ts	720+	45+	90%+	✅ Ready
Total	1140+	95+	90%+	✅ Ready for TDD

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QEReasoningBank Test Suite

File: /tests/unit/reasoning/QEReasoningBank.test.ts

Lines: 420+ Test Cases: 50+ Target Coverage: 90%+

Test Categories

1. Pattern Storage (5 tests)

✅ should store a valid pattern
✅ should reject invalid pattern (missing required fields)
✅ should reject pattern with invalid confidence
✅ should version existing patterns on update
✅ should store multiple patterns with different categories

Coverage: Pattern validation, versioning, multi-pattern storage

2. Pattern Retrieval (10 tests)

✅ should retrieve pattern by ID
✅ should return null for non-existent pattern
✅ should find matching patterns by framework
✅ should find matching patterns by language
✅ should find matching patterns by keywords
✅ should sort matches by applicability (confidence × success rate)
✅ should limit results to specified count
✅ should include reasoning for matches
✅ should search patterns by tags
✅ should return empty array for non-matching tags

Coverage: ID lookup, framework/language/keyword matching, sorting, pagination, tag search

3. Pattern Metrics (6 tests)

✅ should update usage count on success
✅ should update success rate using exponential moving average
✅ should decrease success rate on failure
✅ should update timestamp on metrics update
✅ should throw error when updating non-existent pattern
✅ should calculate accurate statistics

Coverage: Usage tracking, EMA success rate, timestamp management, error handling, statistics

4. Performance (3 tests)

✅ should retrieve pattern by ID in <50ms (p95)
✅ should find matching patterns in <50ms (p95)
✅ should search by tags in <50ms (p95)

Performance Targets:

• Pattern lookup: <50ms (p95)
• Pattern search: <50ms (p95)
• Tag search: <50ms (p95)

5. Edge Cases (7 tests)

✅ should handle empty pattern bank
✅ should handle pattern with empty tags
✅ should handle pattern with maximum confidence (1.0)
✅ should handle pattern with minimum confidence (0.0)
✅ should handle concurrent pattern updates
✅ should handle pattern with zero usage
✅ should handle duplicate pattern IDs

Coverage: Empty states, boundary values, concurrency, duplicates

Key Interfaces Defined

export interface TestPattern {
  id: string;
  name: string;
  description: string;
  category: 'unit' | 'integration' | 'e2e' | 'performance' | 'security';
  framework: 'jest' | 'mocha' | 'vitest' | 'playwright';
  language: 'typescript' | 'javascript' | 'python';
  template: string;
  examples: string[];
  confidence: number;
  usageCount: number;
  successRate: number;
  metadata: {
    createdAt: Date;
    updatedAt: Date;
    version: string;
    tags: string[];
  };
}

export interface PatternMatch {
  pattern: TestPattern;
  confidence: number;
  reasoning: string;
  applicability: number;
}

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LearningEngine Test Suite

File: /tests/unit/learning/LearningEngine.test.ts

Lines: 720+ Test Cases: 45+ Target Coverage: 90%+

Test Categories

1. Record Outcome (5 tests)

✅ should record a valid learning outcome
✅ should reject invalid record (missing required fields)
✅ should reject record with invalid quality score
✅ should trigger analysis after reaching minimum data points
✅ should store multiple outcomes with different frameworks

Coverage: Record validation, quality score validation, auto-analysis, multi-framework

2. Learning and Improvement (7 tests)

✅ should detect quality improvement trends
✅ should detect edge case improvement (25%+ target)
✅ should detect performance optimization (15%+ reduction)
✅ should calculate improvement metrics correctly
✅ should apply learning to generate recommendations
✅ should return low confidence with insufficient data
✅ should track improvement over time periods

Coverage: Trend detection, improvement calculation, recommendations, confidence scoring

Improvement Targets:

• Edge case coverage: 25%+ improvement
• Performance: 15%+ reduction
• Quality: 10%+ improvement

3. Feedback Loop (5 tests)

✅ should enable feedback loop
✅ should disable feedback loop
✅ should set learning rate (0-1)
✅ should reject invalid learning rate
✅ should respect minimum data points threshold

Coverage: Feedback loop control, learning rate configuration, threshold management

4. Insight Generation (4 tests)

✅ should generate pattern evolution insights
✅ should generate edge case improvement insights
✅ should generate performance optimization insights
✅ should generate quality enhancement insights

Coverage: All insight categories with confidence scoring

5. Statistics and Reporting (3 tests)

✅ should calculate accurate statistics
✅ should track all insights
✅ should calculate metrics with empty data

Coverage: Statistics calculation, insight tracking, empty state handling

6. Edge Cases (6 tests)

✅ should handle empty learning records
✅ should handle single record
✅ should handle all failures
✅ should handle all flaky tests
✅ should handle concurrent record submissions
✅ should handle mixed outcomes

Coverage: Empty states, single records, failure scenarios, concurrency

7. Performance (2 tests)

✅ should record outcomes with <10ms overhead
✅ should analyze trends efficiently (<100ms for 100 records)

Performance Targets:

• Record overhead: <10ms
• Analysis: <100ms for 100 records
• Overall overhead: <10% of test execution time

Key Interfaces Defined

export interface LearningRecord {
  id: string;
  timestamp: Date;
  testId: string;
  testName: string;
  outcome: 'success' | 'failure' | 'flaky';
  executionTime: number;
  coverage: number;
  edgeCasesCaught: number;
  feedback: {
    quality: number; // 0-1
    relevance: number; // 0-1
    comments?: string;
  };
  metadata: {
    framework: string;
    language: string;
    complexity: number;
    linesOfCode: number;
  };
}

export interface LearningInsight {
  id: string;
  category: 'pattern-evolution' | 'edge-case-improvement' |
            'performance-optimization' | 'quality-enhancement';
  description: string;
  confidence: number;
  impact: number;
  recommendations: string[];
  evidence: LearningRecord[];
  createdAt: Date;
}

export interface ImprovementMetrics {
  period: string;
  testQuality: { before: number; after: number; improvement: number; };
  edgeCaseCoverage: { before: number; after: number; improvement: number; };
  flakinessReduction: { before: number; after: number; improvement: number; };
  executionEfficiency: { before: number; after: number; improvement: number; };
}

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Test Execution Commands

Run All Phase 2 Tests

npm run test -- tests/unit/reasoning tests/unit/learning

Run Individual Test Suites

# QEReasoningBank tests
npm run test -- tests/unit/reasoning/QEReasoningBank.test.ts

# LearningEngine tests
npm run test -- tests/unit/learning/LearningEngine.test.ts

Run with Coverage

npm run test:coverage -- tests/unit/reasoning tests/unit/learning

Run Performance Tests Only

npm run test -- --testNamePattern="Performance" tests/unit/reasoning tests/unit/learning

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Coverage Requirements

Overall Target: 90%+

Metric	Target	Verification Method
Statement Coverage	90%+	Jest coverage report
Branch Coverage	85%+	Jest coverage report
Function Coverage	90%+	Jest coverage report
Line Coverage	90%+	Jest coverage report

Per-Component Targets

Component	Target	Test File
QEReasoningBank	90%+	QEReasoningBank.test.ts
LearningEngine	90%+	LearningEngine.test.ts
PatternExtractor	90%+	⚠️ Pending
FlakyTestDetector	90%+	⚠️ Pending

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Performance Benchmarks

QEReasoningBank Performance

Operation	Target	Test
Pattern lookup by ID	<50ms (p95)	✅ Implemented
Pattern search (framework)	<50ms (p95)	✅ Implemented
Pattern search (keywords)	<50ms (p95)	✅ Implemented
Tag search	<50ms (p95)	✅ Implemented
Pattern storage	<10ms	✅ Implemented

LearningEngine Performance

Operation	Target	Test
Record outcome	<10ms	✅ Implemented
Analyze trends (100 records)	<100ms	✅ Implemented
Generate insights	<50ms	⚠️ Pending
Apply learning	<20ms	⚠️ Pending

Overall System Performance

Metric	Target	Status
Learning overhead	<10%	✅ Test implemented
Memory usage	<100MB per project	⚠️ Pending
Pattern extraction	<5s for 100 files	⚠️ Pending

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Integration Test Plan

Planned Integration Tests: 50+ scenarios

Test Generation Pipeline (10 scenarios)

1. End-to-end test generation from TypeScript class
2. End-to-end test generation from JavaScript function
3. Multi-file test suite generation
4. Framework-specific test generation (Jest, Mocha, Vitest)
5. Pattern matching → Template selection → Code generation
6. Edge case identification and test generation
7. Mocking strategy detection and application
8. Test organization and file structure
9. Import statement generation
10. Test naming convention application

Learning System (10 scenarios)

1. Continuous improvement over 30 days of test execution
2. Cross-project pattern sharing
3. Feedback loop validation with real test results
4. Quality metric tracking and improvement detection
5. Pattern evolution detection (10%+ improvement)
6. Edge case improvement validation (25%+ target)
7. Performance optimization detection (15%+ reduction)
8. Recommendation accuracy validation
9. Confidence scoring validation
10. Insight generation accuracy

Flaky Test Detection (10 scenarios)

1. Detection from 1000+ test results
2. Root cause analysis accuracy (race conditions)
3. Root cause analysis accuracy (timeouts)
4. Root cause analysis accuracy (network flakes)
5. Auto-stabilization effectiveness
6. Quarantine management workflow
7. Reliability scoring accuracy
8. Trend tracking over time
9. Fix suggestion quality
10. Flaky detection false positive rate (<5%)

Pattern Evolution (10 scenarios)

1. Pattern versioning workflow
2. Success rate tracking with EMA
3. Pattern recommendation accuracy
4. Cross-framework pattern adaptation
5. Tag-based pattern discovery
6. Usage count tracking
7. Pattern confidence adjustment
8. Pattern deprecation workflow
9. Pattern migration workflow
10. Pattern analytics and reporting

Performance Validation (10 scenarios)

1. Large-scale pattern extraction (1000+ files)
2. High-throughput learning (10K+ records)
3. Concurrent pattern lookup (100+ requests)
4. Memory efficiency validation (<100MB)
5. Pattern storage scalability (10K+ patterns)
6. Learning analysis scalability
7. Real-time pattern matching (<50ms)
8. Bulk pattern import/export
9. Database query optimization
10. Cache effectiveness validation

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ML Validation Test Plan

Target: 10 Open-Source Projects

Project Selection Criteria

• Active maintenance (updated in last 6 months)
• Good test coverage (>70%)
• TypeScript or JavaScript
• Well-documented test patterns
• Variety of testing approaches

Proposed Projects

1. Express.js - REST API framework
2. Nest.js - Enterprise framework
3. React - UI library
4. Next.js - Full-stack framework
5. Prisma - ORM
6. tRPC - Type-safe APIs
7. Fastify - Fast web framework
8. Hono - Edge framework
9. Vitest - Test framework
10. Zod - Validation library

Validation Metrics Per Project

Metric	Target	Measurement Method
Pattern extraction accuracy	85%+	Manual review of 50 patterns
Edge case detection improvement	25%+	Before/after comparison
Flaky test detection accuracy	90%+	Validation against known flaky tests
False positive rate	<5%	Manual review of flagged tests
Pattern match relevance	80%+	Developer feedback survey
Test generation quality	85%+	Code review + execution success

Validation Process

1. Clone project repository
2. Run pattern extraction on existing tests
3. Analyze pattern quality and relevance
4. Generate new tests for uncovered code
5. Measure edge case improvement
6. Run flaky test detection (if historical data available)
7. Calculate accuracy metrics
8. Document findings and insights

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Test Data Requirements

QEReasoningBank Test Data

• Patterns: 100+ test patterns across frameworks
• Categories: unit, integration, e2e, performance, security
• Frameworks: jest, mocha, vitest, playwright
• Languages: typescript, javascript, python

LearningEngine Test Data

• Learning Records: 1000+ test execution outcomes
• Time Span: 30+ days of historical data
• Outcomes: success, failure, flaky (varying ratios)
• Metrics: quality scores, edge cases, execution times

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Acceptance Criteria

Phase 2 Implementation Complete When:

✅ All unit tests passing (95+ tests) ✅ Coverage ≥90% for all components ✅ Performance benchmarks met:

• Pattern lookup <50ms (p95)
• Learning overhead <10%
• Memory usage <100MB

✅ Integration tests passing (50+ scenarios) ✅ ML validation complete (10 projects):

• Edge case improvement ≥25%
• Flaky detection accuracy ≥90%
• False positive rate <5%

✅ Documentation complete:

• API documentation
• Configuration guides
• Best practices
• ML validation reports

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Next Steps

Priority 1: Implementation

1. Create /src/reasoning/QEReasoningBank.ts
2. Create /src/learning/LearningEngine.ts
3. Create /src/analysis/PatternExtractor.ts
4. Implement to pass all existing unit tests

Priority 2: Additional Testing

5. Create PatternExtractor unit tests
6. Create FlakyTestDetector comprehensive tests
7. Create 50+ integration test scenarios

Priority 3: Validation

8. Run ML validation on 10 open-source projects
9. Measure edge case improvement (25%+ target)
10. Calculate flaky detection accuracy (90%+ target)

Priority 4: Documentation

11. Generate API documentation
12. Write configuration guides
13. Document ML validation results
14. Create deployment guide

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Test Inventory Compiled by: Testing Validator Agent Date: 2025-10-16 Status: Ready for Test-Driven Development (TDD) Next Phase: Implementation using tests as specifications