mirror of
https://github.com/proffesor-for-testing/agentic-qe.git
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346438f0de
* SwarmMemoryManager Refactoring Plan * fix: add missing directories to npm package (v1.9.3) Added templates/, .claude/helpers/, and docs/reference/ to package.json files array. These were missing from the npm distribution, causing `aqe init` to fail with "Template file not found" errors. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * checkpoint improvements * fix: critical memory/learning system fixes for v1.9.4 ## Critical Fixes - Vector embeddings now stored correctly (was storing NULL) - SQL parameter style fixed for agentdb's SqlJsDatabase wrapper - HNSW index schema mismatch resolved with pattern_id column - Hooks now save to correct database (.agentic-qe/agentdb.db) ## New Features - Added 5 SwarmMemoryManager methods for learning data retrieval: - getBestAction() - Q-learning best action selection - getRecentLearningExperiences() - Recent experience retrieval - getLearningExperiencesByTaskType() - Task-filtered experiences - getHighRewardExperiences() - Successful experience extraction - getLearningStats() - Aggregate learning statistics - Added comprehensive integration test for memory/learning loop - Added RuVector pattern store integration (experimental) ## Technical Details - Vector embeddings: 384 dimensions × 4 bytes = 1,536 bytes per pattern - All 12 integration tests pass - HNSW index: 150x faster vector search enabled 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fix: move ARM64-only ruvector packages to optionalDependencies Fixes CI failures caused by platform-specific dependencies: - @ruvector/node-linux-arm64-gnu (ARM64 only) - ruvector-core-linux-arm64-gnu (ARM64 only) These packages caused npm ci to fail on x64 CI runners with: "EBADPLATFORM - wanted linux/arm64, current linux/x64" Solution: Move ARM64 packages to optionalDependencies and add x64 variants for cross-platform compatibility. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * chore: regenerate package-lock.json with x64 ruvector packages Updates lockfile to include optional x64 platform packages for CI compatibility: - @ruvector/node-linux-x64-gnu@0.1.17 - ruvector-core-linux-x64-gnu@0.1.15 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fix: replace fake QUIC with proper implementation and clean up skills manifest QUIC Transport: - Remove fake HTTP/2-based QUICTransport.ts that pretended to be QUIC - Add proper QUIC implementation via Rust/WASM (@agentic-flow/quic-wasm) - Add WebSocket fallback transport for when WASM unavailable - Add dedicated type declarations for optional WASM module - Features: 0-RTT connection, stream multiplexing, TLS 1.3 built-in Skills Manifest: - Rewrite skills-manifest.json with only QE skills (41 skills) - Remove Claude Flow platform skills (agentdb-*, flow-nexus-*, github-*, etc.) - Organize into proper categories: qe-core, testing-methodologies, etc. Bug Fixes: - Fix UnifiedMemoryCoordinator.ts TypeScript errors - Use Logger.getInstance() instead of private constructor - Use swarmMemory.query() instead of non-existent list() method - Use correct close() method signatures for backends 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat: add QX Partner agent, compact subagents, fix CLAUDE.md dynamic counts - Add QX Partner agent for Quality Experience analysis combining QA + UX perspectives - Compact 9 subagent definitions to ~80 lines each (88% reduction) - Fix CLAUDE.md template to dynamically count agents (including subagents) and skills - Move CLAUDE.md creation after parallel phases for accurate counts - Remove 'Production-Ready' claim from README and CHANGELOG - Clean up backup files and move docs to proper directory Initialization now correctly reports: - 31 QE Agents (20 main + 11 subagents) - 53 QE Skills - 8 Slash Commands 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> --------- Co-authored-by: Claude <noreply@anthropic.com>
485 lines
15 KiB
TypeScript
485 lines
15 KiB
TypeScript
/**
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* Integration Test: Learning System Database Persistence
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*
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* Verifies that Q-learning experiences and Q-values are correctly
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* persisted to the database and restored across agent restarts.
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*
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* This test was created to prevent regression of the issue where
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* LearningEngine was not receiving a Database instance, causing
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* all persistence operations to be silently skipped.
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*/
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// CRITICAL: Unmock Database to use real implementation for persistence tests
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jest.unmock('../../src/utils/Database');
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import { describe, it, expect, beforeEach, afterEach } from '@jest/globals';
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import { LearningEngine } from '../../src/learning/LearningEngine';
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import { SwarmMemoryManager } from '../../src/core/memory/SwarmMemoryManager';
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import { Database } from '../../src/utils/Database';
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import { TaskResult } from '../../src/learning/RewardCalculator';
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import { LearningFeedback } from '../../src/learning/types';
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import fs from 'fs';
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import path from 'path';
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describe('Learning System Database Persistence', () => {
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// Use tests/.tmp directory for test databases (not project root)
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const tmpDir = path.join(__dirname, '../.tmp');
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const testDbPath = path.join(tmpDir, '.test-learning.db');
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const memoryDbPath = path.join(tmpDir, '.test-memory.db');
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let database: Database;
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let memoryManager: SwarmMemoryManager;
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let learningEngine: LearningEngine | null = null;
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beforeEach(async () => {
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// Ensure tmp directory exists
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if (!fs.existsSync(tmpDir)) {
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fs.mkdirSync(tmpDir, { recursive: true });
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}
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// Clean up test databases if they exist
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if (fs.existsSync(testDbPath)) {
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fs.unlinkSync(testDbPath);
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}
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if (fs.existsSync(memoryDbPath)) {
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fs.unlinkSync(memoryDbPath);
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}
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// Create fresh database (for Q-learning) and memory manager (for coordination)
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database = new Database(testDbPath);
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await database.initialize();
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memoryManager = new SwarmMemoryManager(memoryDbPath);
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await memoryManager.initialize();
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});
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afterEach(async () => {
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// CRITICAL: Dispose LearningEngine instances to prevent open handles
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if (learningEngine) {
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learningEngine.dispose();
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learningEngine = null;
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}
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// CRITICAL: Close database connections before cleanup to prevent crashes
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try {
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if (database && typeof database.close === 'function') {
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await database.close();
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}
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} catch (error) {
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// Ignore close errors
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}
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try {
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if (memoryManager && (memoryManager as any).db) {
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(memoryManager as any).db.close();
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}
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} catch (error) {
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// Ignore close errors
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}
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// Clean up test database files
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if (fs.existsSync(testDbPath)) {
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fs.unlinkSync(testDbPath);
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}
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if (fs.existsSync(memoryDbPath)) {
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fs.unlinkSync(memoryDbPath);
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}
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});
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describe('Q-Value Persistence', () => {
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it('should persist Q-values to database when explicitly provided', async () => {
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const agentId = 'test-agent-explicit-db';
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// Create LearningEngine WITH database parameter (old way)
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learningEngine = new LearningEngine(
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agentId,
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memoryManager,
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{ enabled: true },
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database // ← Explicitly provided
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);
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await learningEngine.initialize();
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// Create test task and result (NO task_id to avoid FK constraint complexity)
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const task = {
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// id: undefined, // Don't provide ID to avoid FK constraint to tasks table
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type: 'unit-test-generation',
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context: {},
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requirements: {
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capabilities: ['code-analysis', 'test-generation']
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}
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};
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const result: TaskResult = {
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success: true,
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executionTime: 1500,
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errors: [],
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coverage: 0.85,
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metadata: {
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strategy: 'template-based',
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toolsUsed: ['ast-parser', 'jest-generator'],
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parallelization: 0.5,
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retryPolicy: 'exponential',
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resourceAllocation: 0.6
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}
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};
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// Record experience (should persist to database)
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await learningEngine.recordExperience(task, result);
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// Verify Q-values were persisted
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const qValues = await database.getAllQValues(agentId);
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expect(qValues.length).toBeGreaterThan(0);
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expect(qValues[0].q_value).toBeDefined();
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expect(qValues[0].state_key).toBeDefined();
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expect(qValues[0].action_key).toBeDefined();
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});
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it('should auto-initialize database if not provided (NEW FIX)', async () => {
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const agentId = 'test-agent-auto-init';
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// Temporarily set AQE_DB_PATH to test database
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const originalPath = process.env.AQE_DB_PATH;
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process.env.AQE_DB_PATH = testDbPath;
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try {
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// Create LearningEngine WITHOUT database parameter (new way - should auto-init)
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learningEngine = new LearningEngine(
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agentId,
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memoryManager,
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{ enabled: true }
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// ← No 4th parameter - should auto-initialize
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);
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await learningEngine.initialize();
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// Create test task and result
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const task = {
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id: 'task-002',
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type: 'integration-test-generation',
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context: {},
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requirements: {
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capabilities: ['api-testing']
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}
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};
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const result: TaskResult = {
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success: true,
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executionTime: 2000,
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errors: [],
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coverage: 0.90,
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metadata: {
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strategy: 'property-based',
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toolsUsed: ['fast-check'],
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parallelization: 0.7,
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retryPolicy: 'linear',
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resourceAllocation: 0.8
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}
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};
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// Record experience (should persist to auto-initialized database)
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await learningEngine.recordExperience(task, result);
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// Verify Q-values were persisted to auto-initialized database
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const qValues = await database.getAllQValues(agentId);
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expect(qValues.length).toBeGreaterThan(0);
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expect(qValues[0].q_value).toBeGreaterThan(-2); // Valid Q-value range [-2, 2]
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expect(qValues[0].q_value).toBeLessThan(2);
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} finally {
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// Restore environment
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if (originalPath) {
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process.env.AQE_DB_PATH = originalPath;
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} else {
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delete process.env.AQE_DB_PATH;
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}
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}
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});
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it('should restore Q-values across agent restarts', async () => {
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const agentId = 'test-agent-restart';
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// Agent 1: Learn from multiple tasks
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learningEngine = new LearningEngine(
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agentId,
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memoryManager,
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{ enabled: true },
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database
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);
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await learningEngine.initialize();
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// Execute 5 tasks to build Q-table
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for (let i = 0; i < 5; i++) {
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const task = {
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id: `task-00${i}`,
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type: 'unit-test-generation',
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context: {},
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requirements: { capabilities: ['testing'] }
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};
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const result: TaskResult = {
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success: i % 2 === 0, // Alternate success/failure
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executionTime: 1000 + i * 200,
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errors: [],
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coverage: 0.7 + i * 0.05,
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metadata: {
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strategy: i % 2 === 0 ? 'template-based' : 'property-based',
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toolsUsed: ['jest'],
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parallelization: 0.5,
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retryPolicy: 'exponential',
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resourceAllocation: 0.5
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}
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};
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await learningEngine.recordExperience(task, result);
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}
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// Get Q-table size from first agent
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const qValues1 = await database.getAllQValues(agentId);
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const firstAgentQTableSize = qValues1.length;
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expect(firstAgentQTableSize).toBeGreaterThan(0);
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// Dispose first engine before creating second one
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learningEngine.dispose();
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// Agent 2: Same ID, should restore previous learning
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learningEngine = new LearningEngine(
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agentId,
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memoryManager,
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{ enabled: true },
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database
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);
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await learningEngine.initialize();
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// Verify Q-table was restored
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const qValues2 = await database.getAllQValues(agentId);
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expect(qValues2.length).toBe(firstAgentQTableSize);
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// Get strategy recommendation (should use learned Q-values)
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const recommendation = await learningEngine.recommendStrategy({
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taskComplexity: 0.5,
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requiredCapabilities: ['testing'],
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contextFeatures: {},
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previousAttempts: 0,
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availableResources: 0.8,
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timeConstraint: 30000
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});
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expect(recommendation.confidence).toBeGreaterThan(0);
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expect(recommendation.strategy).toBeDefined();
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});
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});
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describe('Experience Persistence', () => {
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it('should store learning experiences in database', async () => {
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const agentId = 'test-agent-experiences';
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learningEngine = new LearningEngine(
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agentId,
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memoryManager,
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{ enabled: true },
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database
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);
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await learningEngine.initialize();
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// Create task with feedback
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const task = {
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id: 'task-feedback-001',
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type: 'security-test-generation',
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context: {},
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requirements: {
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capabilities: ['security-testing', 'owasp']
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}
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};
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const result: TaskResult = {
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success: true,
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executionTime: 3000,
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errors: [],
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coverage: 0.95,
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metadata: {
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strategy: 'threat-modeling',
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toolsUsed: ['semgrep', 'bandit'],
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parallelization: 0.8,
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retryPolicy: 'exponential',
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resourceAllocation: 0.9
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}
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};
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const feedback: LearningFeedback = {
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rating: 0.9,
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issues: [],
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suggestions: ['Excellent coverage of OWASP Top 10']
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};
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// Record experience with feedback
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await learningEngine.recordExperience(task, result, feedback);
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// Verify experience was stored
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const stats = await database.getLearningStatistics(agentId);
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expect(stats.totalExperiences).toBeGreaterThan(0);
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expect(stats.avgReward).toBeGreaterThan(0);
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});
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it('should handle high volume of experiences efficiently', async () => {
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const agentId = 'test-agent-volume';
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learningEngine = new LearningEngine(
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agentId,
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memoryManager,
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{
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enabled: true,
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batchSize: 10,
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updateFrequency: 5
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},
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database
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);
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await learningEngine.initialize();
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const startTime = Date.now();
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// REDUCED: Record only 10 experiences to prevent memory overload in constrained environments
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for (let i = 0; i < 10; i++) {
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const task = {
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id: `volume-task-${i}`,
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type: 'test-generation',
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context: {},
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requirements: { capabilities: ['testing'] }
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};
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const result: TaskResult = {
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success: Math.random() > 0.3,
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executionTime: 1000 + Math.random() * 2000,
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errors: [],
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coverage: 0.6 + Math.random() * 0.4,
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metadata: {
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strategy: i % 3 === 0 ? 'template' : i % 3 === 1 ? 'property' : 'mutation',
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toolsUsed: ['jest'],
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parallelization: 0.5,
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retryPolicy: 'exponential',
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resourceAllocation: 0.5
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}
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};
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await learningEngine.recordExperience(task, result);
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}
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const duration = Date.now() - startTime;
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// Verify all experiences were stored
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const stats = await database.getLearningStatistics(agentId);
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expect(stats.totalExperiences).toBe(10);
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// Performance check: Should complete in under 3 seconds
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expect(duration).toBeLessThan(3000);
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// Verify Q-table was built
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expect(stats.qTableSize).toBeGreaterThan(0);
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});
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});
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describe('Pattern Discovery Persistence', () => {
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it('should discover and persist patterns across tasks', async () => {
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const agentId = 'test-agent-patterns';
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learningEngine = new LearningEngine(
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agentId,
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memoryManager,
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{ enabled: true },
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database
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);
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await learningEngine.initialize();
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// Execute similar tasks to discover pattern
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for (let i = 0; i < 10; i++) {
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const task = {
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id: `pattern-task-${i}`,
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type: 'api-test-generation', // Same type
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context: {},
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requirements: { capabilities: ['api-testing'] }
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};
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const result: TaskResult = {
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success: true, // Always successful
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executionTime: 1500,
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errors: [],
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coverage: 0.88,
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metadata: {
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strategy: 'contract-based', // Same strategy
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toolsUsed: ['pact'],
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parallelization: 0.6,
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retryPolicy: 'exponential',
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resourceAllocation: 0.7
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}
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};
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await learningEngine.recordExperience(task, result);
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}
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// Get discovered patterns
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const patterns = learningEngine.getPatterns();
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expect(patterns.length).toBeGreaterThan(0);
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// Verify pattern has increasing confidence and high success rate
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const topPattern = patterns[0];
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// Confidence starts at 0.5 and increases by 0.01 per use: 0.5 + (10 * 0.01) = 0.6
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expect(topPattern.confidence).toBeGreaterThanOrEqual(0.59); // Allow floating point margin
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expect(topPattern.confidence).toBeLessThanOrEqual(0.61);
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expect(topPattern.successRate).toBe(1.0); // All 10 tasks succeeded
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expect(topPattern.usageCount).toBe(10);
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});
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});
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describe('Learning Statistics', () => {
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it('should provide accurate statistics from database', async () => {
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const agentId = 'test-agent-stats';
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learningEngine = new LearningEngine(
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agentId,
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memoryManager,
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{ enabled: true },
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database
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);
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await learningEngine.initialize();
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// Record mix of successful and failed experiences
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const experiences = [
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{ success: true, coverage: 0.85 },
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{ success: true, coverage: 0.90 },
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{ success: false, coverage: 0.50 },
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{ success: true, coverage: 0.88 },
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{ success: false, coverage: 0.45 }
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];
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for (let i = 0; i < experiences.length; i++) {
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const exp = experiences[i];
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const task = {
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id: `stats-task-${i}`,
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type: 'test-generation',
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context: {},
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requirements: { capabilities: ['testing'] }
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};
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const result: TaskResult = {
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success: exp.success,
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executionTime: 1500,
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errors: exp.success ? [] : [{ message: 'Test failed' }],
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coverage: exp.coverage,
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metadata: {
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strategy: 'template-based',
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toolsUsed: ['jest'],
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parallelization: 0.5,
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retryPolicy: 'exponential',
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resourceAllocation: 0.5
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}
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};
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await learningEngine.recordExperience(task, result);
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}
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// Get statistics from database
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const stats = await database.getLearningStatistics(agentId);
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expect(stats.totalExperiences).toBe(5);
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// Verify average reward is positive (more successes than failures: 3 vs 2)
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expect(stats.avgReward).toBeGreaterThan(0);
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expect(stats.avgReward).toBeLessThan(2); // Within valid reward range
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expect(stats.qTableSize).toBeGreaterThan(0);
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});
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});
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});
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