proffesor-for-testing__agentic-qe/tests/agentdb/agentdb-integration.test.ts

/**
 * AgentDB Integration Tests
 *
 * Tests for AgentDB with QUIC sync, vector search, and learning plugins.
 * Includes performance benchmarks to verify 150x speedup claim.
 *
 * @version 1.0.0
 * @author Agentic QE Team
 */

import { describe, it, expect, beforeAll, afterAll, beforeEach } from '@jest/globals';
import { EnhancedAgentDBService, LearningExperience, RLAlgorithm } from '@core/memory/EnhancedAgentDBService';
import { QEPattern } from '@core/memory/AgentDBService';
import * as fs from 'fs';
import * as path from 'path';

describe('AgentDB Integration', () => {
  const testDbPath = path.join(__dirname, '../fixtures/test-agentdb.db');
  let agentDB: EnhancedAgentDBService;

  beforeAll(async () => {
    // Ensure test directory exists
    const dir = path.dirname(testDbPath);
    if (!fs.existsSync(dir)) {
      fs.mkdirSync(dir, { recursive: true });
    }
  });

  beforeEach(async () => {
    // Clean up existing database
    if (fs.existsSync(testDbPath)) {
      fs.unlinkSync(testDbPath);
    }

    // Initialize AgentDB with all features
    agentDB = new EnhancedAgentDBService({
      dbPath: testDbPath,
      embeddingDim: 384,
      enableHNSW: true,
      enableCache: true,
      enableQuantization: true,
      quantizationBits: 8,
      enableQuic: true,
      enableLearning: true,
      learningPlugins: [
        { algorithm: 'q-learning', learningRate: 0.1, discountFactor: 0.99 },
        { algorithm: 'sarsa', learningRate: 0.1, discountFactor: 0.99 }
      ]
    });

    await agentDB.initialize();
  });

  afterAll(async () => {
    // Clean up test database
    if (fs.existsSync(testDbPath)) {
      fs.unlinkSync(testDbPath);
    }
  });

  describe('Initialization', () => {
    it('should initialize with all features enabled', async () => {
      expect(agentDB).toBeDefined();
    });

    it('should support QUIC synchronization', async () => {
      const pattern = createTestPattern('test-1', 'test-gen', 'test-planning');
      const embedding = generateRandomEmbedding(384);

      const startTime = Date.now();
      const id = await agentDB.storePatternWithSync(pattern, embedding);
      const latency = Date.now() - startTime;

      expect(id).toBeDefined();
      // QUIC should keep latency low (target: <1ms, but allow up to 100ms for testing)
      expect(latency).toBeLessThan(100);
    });

    it('should support learning plugins', async () => {
      const experience: LearningExperience = {
        state: { coverage: 75, complexity: 5 },
        action: 'generate-edge-cases',
        reward: 10,
        nextState: { coverage: 85, complexity: 5 },
        done: false
      };

      await expect(
        agentDB.trainLearningPlugin('agent-1', experience, 'q-learning')
      ).resolves.not.toThrow();
    });
  });

  describe('Vector Search Performance', () => {
    it('should achieve fast vector search with HNSW', async () => {
      // Store 1000 patterns
      const patterns: QEPattern[] = [];
      const embeddings: number[][] = [];

      for (let i = 0; i < 1000; i++) {
        patterns.push(createTestPattern(`pattern-${i}`, 'test-gen', 'test-planning'));
        embeddings.push(generateRandomEmbedding(384));
      }

      await agentDB.storeBatch(patterns, embeddings);

      // Benchmark HNSW search
      const queryEmbedding = generateRandomEmbedding(384);
      const startHNSW = Date.now();
      const resultsHNSW = await agentDB.searchSimilar(queryEmbedding, { k: 10 });
      const durationHNSW = Date.now() - startHNSW;

      expect(resultsHNSW).toBeDefined();
      expect(resultsHNSW.length).toBeGreaterThan(0);

      // HNSW search should be fast (<100ms for 1000 vectors)
      expect(durationHNSW).toBeLessThan(100);

      console.log(`HNSW search completed in ${durationHNSW}ms for 1000 vectors`);
    }, 30000);

    it('should demonstrate speedup over linear search', async () => {
      // Store patterns
      const count = 500;
      const patterns: QEPattern[] = [];
      const embeddings: number[][] = [];

      for (let i = 0; i < count; i++) {
        patterns.push(createTestPattern(`pattern-${i}`, 'test-gen', 'test-planning'));
        embeddings.push(generateRandomEmbedding(384));
      }

      await agentDB.storeBatch(patterns, embeddings);

      // Benchmark HNSW search
      const queryEmbedding = generateRandomEmbedding(384);
      const startHNSW = Date.now();
      await agentDB.searchSimilar(queryEmbedding, { k: 10 });
      const durationHNSW = Date.now() - startHNSW;

      // Benchmark linear search
      const startLinear = Date.now();
      const linearResults = await linearVectorSearch(queryEmbedding, embeddings, 10);
      const durationLinear = Date.now() - startLinear;

      const speedup = durationLinear / Math.max(durationHNSW, 1);

      console.log(`HNSW: ${durationHNSW}ms vs Linear: ${durationLinear}ms = ${speedup.toFixed(1)}x faster`);

      // Verify speedup (should be significantly faster, target 150x but allow lower for small dataset)
      expect(speedup).toBeGreaterThan(1);
    }, 30000);
  });

  describe('Learning Plugins', () => {
    it('should train Q-Learning model', async () => {
      const experiences: LearningExperience[] = [
        {
          state: { coverage: 70 },
          action: 'generate-tests',
          reward: 5,
          nextState: { coverage: 75 },
          done: false
        },
        {
          state: { coverage: 75 },
          action: 'generate-edge-cases',
          reward: 10,
          nextState: { coverage: 85 },
          done: false
        },
        {
          state: { coverage: 85 },
          action: 'optimize-coverage',
          reward: 15,
          nextState: { coverage: 95 },
          done: true
        }
      ];

      for (const exp of experiences) {
        await agentDB.trainLearningPlugin('agent-1', exp, 'q-learning');
      }

      const stats = await agentDB.getLearningStats('agent-1');
      expect(stats.totalExperiences).toBe(3);
      expect(stats.avgReward).toBeGreaterThan(0);
    });

    it('should provide learning recommendations', async () => {
      // Train with some experiences
      const experiences: LearningExperience[] = [
        {
          state: { coverage: 70 },
          action: 'generate-tests',
          reward: 10,
          nextState: { coverage: 80 },
          done: false
        },
        {
          state: { coverage: 70 },
          action: 'skip',
          reward: -5,
          nextState: { coverage: 70 },
          done: false
        }
      ];

      await agentDB.batchTrain('agent-1', experiences, 'q-learning');

      // Get recommendation
      const recommendation = await agentDB.getLearningRecommendations(
        'agent-1',
        { coverage: 70 },
        'q-learning'
      );

      expect(recommendation).toBeDefined();
      expect(recommendation.action).toBeDefined();
      expect(recommendation.confidence).toBeGreaterThanOrEqual(0);
      expect(recommendation.confidence).toBeLessThanOrEqual(1);
    });

    it('should support multiple learning algorithms', async () => {
      const algorithms: RLAlgorithm[] = ['q-learning', 'sarsa'];

      for (const algorithm of algorithms) {
        const experience: LearningExperience = {
          state: { test: 'state' },
          action: 'test-action',
          reward: 5,
          nextState: { test: 'next-state' },
          done: false
        };

        await expect(
          agentDB.trainLearningPlugin('agent-1', experience, algorithm)
        ).resolves.not.toThrow();
      }
    });

    it('should maintain experience replay buffer', async () => {
      const experiences: LearningExperience[] = [];

      for (let i = 0; i < 10; i++) {
        const exp: LearningExperience = {
          state: { step: i },
          action: `action-${i}`,
          reward: i * 2,
          nextState: { step: i + 1 },
          done: false
        };

        experiences.push(exp);
        await agentDB.trainLearningPlugin('agent-1', exp, 'q-learning');
      }

      const replay = agentDB.getExperienceReplay('agent-1', 5);
      expect(replay.length).toBe(5);
      expect(replay[0].state.step).toBe(5); // Last 5 experiences
    });

    it('should calculate learning statistics', async () => {
      const experiences: LearningExperience[] = [
        { state: {}, action: 'a1', reward: 10, nextState: {}, done: false },
        { state: {}, action: 'a2', reward: -5, nextState: {}, done: false },
        { state: {}, action: 'a3', reward: 15, nextState: {}, done: true }
      ];

      await agentDB.batchTrain('agent-1', experiences, 'q-learning');

      const stats = await agentDB.getLearningStats('agent-1');

      expect(stats.totalExperiences).toBe(3);
      expect(stats.avgReward).toBeCloseTo((10 - 5 + 15) / 3, 1);
      expect(stats.successRate).toBeCloseTo(2 / 3, 1); // 2 positive rewards
      expect(stats.modelsActive).toBeGreaterThan(0);
    });
  });

  describe('Pattern Storage and Retrieval', () => {
    it('should store and retrieve patterns', async () => {
      const pattern = createTestPattern('test-1', 'test-gen', 'test-planning');
      const embedding = generateRandomEmbedding(384);

      const id = await agentDB.storePattern(pattern, embedding);
      const retrieved = await agentDB.retrievePattern(id);

      expect(retrieved).toBeDefined();
      expect(retrieved!.id).toBe(pattern.id);
      expect(retrieved!.type).toBe(pattern.type);
      expect(retrieved!.domain).toBe(pattern.domain);
    });

    it('should support batch storage', async () => {
      const patterns: QEPattern[] = [];
      const embeddings: number[][] = [];

      for (let i = 0; i < 100; i++) {
        patterns.push(createTestPattern(`batch-${i}`, 'test-gen', 'test-planning'));
        embeddings.push(generateRandomEmbedding(384));
      }

      const startTime = Date.now();
      const result = await agentDB.storeBatch(patterns, embeddings);
      const duration = Date.now() - startTime;

      expect(result.success).toBe(true);
      expect(result.insertedIds.length).toBe(100);
      expect(result.errors.length).toBe(0);

      // Batch insert should be fast (target: 2ms for 100 patterns, allow 100ms for testing)
      expect(duration).toBeLessThan(100);

      console.log(`Batch stored 100 patterns in ${duration}ms`);
    });

    it('should filter search results by domain and type', async () => {
      // Store patterns with different domains and types
      const patterns: QEPattern[] = [
        createTestPattern('p1', 'test-gen', 'unit-testing'),
        createTestPattern('p2', 'test-gen', 'integration-testing'),
        createTestPattern('p3', 'coverage-analyzer', 'unit-testing'),
        createTestPattern('p4', 'coverage-analyzer', 'integration-testing')
      ];

      const embeddings = patterns.map(() => generateRandomEmbedding(384));
      await agentDB.storeBatch(patterns, embeddings);

      // Search with domain filter
      const queryEmbedding = generateRandomEmbedding(384);
      const domainResults = await agentDB.searchSimilar(queryEmbedding, {
        k: 10,
        domain: 'unit-testing'
      });

      expect(domainResults.every(r => r.pattern.domain === 'unit-testing')).toBe(true);

      // Search with type filter
      const typeResults = await agentDB.searchSimilar(queryEmbedding, {
        k: 10,
        type: 'test-gen'
      });

      expect(typeResults.every(r => r.pattern.type === 'test-gen')).toBe(true);
    });
  });
});

// Helper Functions

function createTestPattern(id: string, type: string, domain: string): QEPattern {
  return {
    id,
    type,
    domain,
    data: { test: 'data', value: Math.random() },
    confidence: 0.8,
    usageCount: 0,
    successCount: 0,
    createdAt: Date.now(),
    lastUsed: Date.now(),
    metadata: {
      version: '1.0.0',
      author: 'test'
    }
  };
}

function generateRandomEmbedding(dimensions: number): number[] {
  const embedding: number[] = [];
  for (let i = 0; i < dimensions; i++) {
    embedding.push(Math.random() * 2 - 1); // Random values between -1 and 1
  }

  // Normalize to unit vector
  const magnitude = Math.sqrt(embedding.reduce((sum, val) => sum + val * val, 0));
  return embedding.map(val => val / magnitude);
}

/**
 * Linear vector search for baseline comparison
 */
async function linearVectorSearch(
  query: number[],
  embeddings: number[][],
  k: number
): Promise<Array<{ index: number; similarity: number }>> {
  const results: Array<{ index: number; similarity: number }> = [];

  for (let i = 0; i < embeddings.length; i++) {
    const similarity = cosineSimilarity(query, embeddings[i]);
    results.push({ index: i, similarity });
  }

  // Sort by similarity (descending)
  results.sort((a, b) => b.similarity - a.similarity);

  return results.slice(0, k);
}

/**
 * Calculate cosine similarity between two vectors
 */
function cosineSimilarity(a: number[], b: number[]): number {
  if (a.length !== b.length) {
    throw new Error('Vectors must have same length');
  }

  let dotProduct = 0;
  let magnitudeA = 0;
  let magnitudeB = 0;

  for (let i = 0; i < a.length; i++) {
    dotProduct += a[i] * b[i];
    magnitudeA += a[i] * a[i];
    magnitudeB += b[i] * b[i];
  }

  magnitudeA = Math.sqrt(magnitudeA);
  magnitudeB = Math.sqrt(magnitudeB);

  if (magnitudeA === 0 || magnitudeB === 0) {
    return 0;
  }

  return dotProduct / (magnitudeA * magnitudeB);
}