chore: remove outdated documentation and reference files

- Remove debugging service documentation (DEBUgging_SERVICES.md)
- Remove development logs policy and quick reference guides
- Remove E2E test creation summary
- Remove gift certificate example file
- Remove GitHub pull summary documentation

tests/testing/test_performance_benchmarks.py

@@ -0,0 +1,512 @@
+"""
+Performance Benchmark Tests for AITBC
+Tests system performance under various loads and conditions
+"""
+
+import pytest
+import time
+import asyncio
+import threading
+from datetime import datetime, timedelta
+from unittest.mock import Mock, patch
+from concurrent.futures import ThreadPoolExecutor
+import statistics
+
+
+class TestAPIPerformance:
+    """Test API endpoint performance"""
+    
+    def test_response_time_benchmarks(self):
+        """Test API response time benchmarks"""
+        # Mock API client
+        client = Mock()
+        
+        # Simulate different response times
+        response_times = [0.05, 0.08, 0.12, 0.06, 0.09, 0.11, 0.07, 0.10]
+        
+        # Calculate performance metrics
+        avg_response_time = statistics.mean(response_times)
+        max_response_time = max(response_times)
+        min_response_time = min(response_times)
+        
+        # Performance assertions
+        assert avg_response_time < 0.1  # Average should be under 100ms
+        assert max_response_time < 0.2  # Max should be under 200ms
+        assert min_response_time > 0.01  # Should be reasonable minimum
+        
+        # Test performance thresholds
+        performance_thresholds = {
+            'excellent': 0.05,   # < 50ms
+            'good': 0.1,         # < 100ms
+            'acceptable': 0.2,   # < 200ms
+            'poor': 0.5          # > 500ms
+        }
+        
+        # Classify performance
+        if avg_response_time < performance_thresholds['excellent']:
+            performance_rating = 'excellent'
+        elif avg_response_time < performance_thresholds['good']:
+            performance_rating = 'good'
+        elif avg_response_time < performance_thresholds['acceptable']:
+            performance_rating = 'acceptable'
+        else:
+            performance_rating = 'poor'
+        
+        assert performance_rating in ['excellent', 'good', 'acceptable']
+    
+    def test_concurrent_request_handling(self):
+        """Test handling of concurrent requests"""
+        # Mock API endpoint
+        def mock_api_call(request_id):
+            time.sleep(0.01)  # Simulate 10ms processing time
+            return {'request_id': request_id, 'status': 'success'}
+        
+        # Test concurrent execution
+        num_requests = 50
+        start_time = time.time()
+        
+        with ThreadPoolExecutor(max_workers=10) as executor:
+            futures = [
+                executor.submit(mock_api_call, i) 
+                for i in range(num_requests)
+            ]
+            results = [future.result() for future in futures]
+        
+        end_time = time.time()
+        total_time = end_time - start_time
+        
+        # Performance assertions
+        assert len(results) == num_requests
+        assert all(result['status'] == 'success' for result in results)
+        assert total_time < 1.0  # Should complete in under 1 second
+        
+        # Calculate throughput
+        throughput = num_requests / total_time
+        assert throughput > 50  # Should handle at least 50 requests per second
+    
+    def test_memory_usage_under_load(self):
+        """Test memory usage under load"""
+        import psutil
+        import os
+        
+        # Get initial memory usage
+        process = psutil.Process(os.getpid())
+        initial_memory = process.memory_info().rss / 1024 / 1024  # MB
+        
+        # Simulate memory-intensive operations
+        data_store = []
+        for i in range(1000):
+            data_store.append({
+                'id': i,
+                'data': 'x' * 1000,  # 1KB per item
+                'timestamp': datetime.utcnow().isoformat()
+            })
+        
+        # Get peak memory usage
+        peak_memory = process.memory_info().rss / 1024 / 1024  # MB
+        memory_increase = peak_memory - initial_memory
+        
+        # Memory assertions
+        assert memory_increase < 100  # Should not increase by more than 100MB
+        assert len(data_store) == 1000
+        
+        # Cleanup
+        del data_store
+
+
+class TestDatabasePerformance:
+    """Test database operation performance"""
+    
+    def test_query_performance(self):
+        """Test database query performance"""
+        # Mock database operations
+        def mock_query(query_type):
+            if query_type == 'simple':
+                time.sleep(0.001)  # 1ms
+            elif query_type == 'complex':
+                time.sleep(0.01)   # 10ms
+            elif query_type == 'aggregate':
+                time.sleep(0.05)   # 50ms
+            return {'results': ['data'], 'query_type': query_type}
+        
+        # Test different query types
+        query_types = ['simple', 'complex', 'aggregate']
+        query_times = {}
+        
+        for query_type in query_types:
+            start_time = time.time()
+            result = mock_query(query_type)
+            end_time = time.time()
+            query_times[query_type] = end_time - start_time
+            
+            assert result['query_type'] == query_type
+        
+        # Performance assertions
+        assert query_times['simple'] < 0.005    # < 5ms
+        assert query_times['complex'] < 0.02    # < 20ms
+        assert query_times['aggregate'] < 0.1   # < 100ms
+    
+    def test_batch_operation_performance(self):
+        """Test batch operation performance"""
+        # Mock batch insert
+        def mock_batch_insert(items):
+            time.sleep(len(items) * 0.001)  # 1ms per item
+            return {'inserted_count': len(items)}
+        
+        # Test different batch sizes
+        batch_sizes = [10, 50, 100, 500]
+        performance_results = {}
+        
+        for batch_size in batch_sizes:
+            items = [{'id': i, 'data': f'item_{i}'} for i in range(batch_size)]
+            
+            start_time = time.time()
+            result = mock_batch_insert(items)
+            end_time = time.time()
+            
+            performance_results[batch_size] = {
+                'time': end_time - start_time,
+                'throughput': batch_size / (end_time - start_time)
+            }
+            
+            assert result['inserted_count'] == batch_size
+        
+        # Performance analysis
+        for batch_size, metrics in performance_results.items():
+            assert metrics['throughput'] > 100  # Should handle at least 100 items/second
+            assert metrics['time'] < 5.0        # Should complete in under 5 seconds
+    
+    def test_connection_pool_performance(self):
+        """Test database connection pool performance"""
+        # Mock connection pool
+        class MockConnectionPool:
+            def __init__(self, max_connections=10):
+                self.max_connections = max_connections
+                self.active_connections = 0
+                self.lock = threading.Lock()
+            
+            def get_connection(self):
+                with self.lock:
+                    if self.active_connections < self.max_connections:
+                        self.active_connections += 1
+                        return MockConnection()
+                    else:
+                        raise Exception("Connection pool exhausted")
+            
+            def release_connection(self, conn):
+                with self.lock:
+                    self.active_connections -= 1
+        
+        class MockConnection:
+            def execute(self, query):
+                time.sleep(0.01)  # 10ms query time
+                return {'result': 'success'}
+        
+        # Test connection pool under load
+        pool = MockConnectionPool(max_connections=5)
+        
+        def worker_task():
+            try:
+                conn = pool.get_connection()
+                result = conn.execute("SELECT * FROM test")
+                pool.release_connection(conn)
+                return result
+            except Exception as e:
+                return {'error': str(e)}
+        
+        # Test concurrent access
+        with ThreadPoolExecutor(max_workers=10) as executor:
+            futures = [executor.submit(worker_task) for _ in range(20)]
+            results = [future.result() for future in futures]
+        
+        # Analyze results
+        successful_results = [r for r in results if 'error' not in r]
+        error_results = [r for r in results if 'error' in r]
+        
+        # Should have some successful and some error results (pool exhaustion)
+        assert len(successful_results) > 0
+        assert len(error_results) > 0
+        assert len(successful_results) + len(error_results) == 20
+
+
+class TestBlockchainPerformance:
+    """Test blockchain operation performance"""
+    
+    def test_transaction_processing_speed(self):
+        """Test transaction processing speed"""
+        # Mock transaction processing
+        def mock_process_transaction(tx):
+            processing_time = 0.1 + (len(tx['data']) * 0.001)  # Base 100ms + data size
+            time.sleep(processing_time)
+            return {
+                'tx_hash': f'0x{hash(str(tx)) % 1000000:x}',
+                'processing_time': processing_time
+            }
+        
+        # Test transactions of different sizes
+        transactions = [
+            {'data': 'small', 'amount': 1.0},
+            {'data': 'x' * 100, 'amount': 10.0},      # 100 bytes
+            {'data': 'x' * 1000, 'amount': 100.0},    # 1KB
+            {'data': 'x' * 10000, 'amount': 1000.0},   # 10KB
+        ]
+        
+        processing_times = []
+        
+        for tx in transactions:
+            start_time = time.time()
+            result = mock_process_transaction(tx)
+            end_time = time.time()
+            
+            processing_times.append(result['processing_time'])
+            assert 'tx_hash' in result
+            assert result['processing_time'] > 0
+        
+        # Performance assertions
+        assert processing_times[0] < 0.2   # Small transaction < 200ms
+        assert processing_times[-1] < 1.0  # Large transaction < 1 second
+    
+    def test_block_validation_performance(self):
+        """Test block validation performance"""
+        # Mock block validation
+        def mock_validate_block(block):
+            num_transactions = len(block['transactions'])
+            validation_time = num_transactions * 0.01  # 10ms per transaction
+            time.sleep(validation_time)
+            return {
+                'valid': True,
+                'validation_time': validation_time,
+                'transactions_validated': num_transactions
+            }
+        
+        # Test blocks with different transaction counts
+        blocks = [
+            {'transactions': [f'tx_{i}' for i in range(10)]},    # 10 transactions
+            {'transactions': [f'tx_{i}' for i in range(50)]},    # 50 transactions
+            {'transactions': [f'tx_{i}' for i in range(100)]},   # 100 transactions
+        ]
+        
+        validation_results = []
+        
+        for block in blocks:
+            start_time = time.time()
+            result = mock_validate_block(block)
+            end_time = time.time()
+            
+            validation_results.append(result)
+            assert result['valid'] is True
+            assert result['transactions_validated'] == len(block['transactions'])
+        
+        # Performance analysis
+        for i, result in enumerate(validation_results):
+            expected_time = len(blocks[i]['transactions']) * 0.01
+            assert abs(result['validation_time'] - expected_time) < 0.01
+    
+    def test_sync_performance(self):
+        """Test blockchain sync performance"""
+        # Mock blockchain sync
+        def mock_sync_blocks(start_block, end_block):
+            num_blocks = end_block - start_block
+            sync_time = num_blocks * 0.05  # 50ms per block
+            time.sleep(sync_time)
+            return {
+                'synced_blocks': num_blocks,
+                'sync_time': sync_time,
+                'blocks_per_second': num_blocks / sync_time
+            }
+        
+        # Test different sync ranges
+        sync_ranges = [
+            (1000, 1010),   # 10 blocks
+            (1000, 1050),   # 50 blocks
+            (1000, 1100),   # 100 blocks
+        ]
+        
+        sync_results = []
+        
+        for start, end in sync_ranges:
+            result = mock_sync_blocks(start, end)
+            sync_results.append(result)
+            
+            assert result['synced_blocks'] == (end - start)
+            assert result['blocks_per_second'] > 10  # Should sync at least 10 blocks/second
+        
+        # Performance consistency
+        sync_rates = [result['blocks_per_second'] for result in sync_results]
+        avg_sync_rate = statistics.mean(sync_rates)
+        assert avg_sync_rate > 15  # Average should be at least 15 blocks/second
+
+
+class TestSystemResourcePerformance:
+    """Test system resource utilization"""
+    
+    def test_cpu_utilization(self):
+        """Test CPU utilization under load"""
+        import psutil
+        import os
+        
+        # Get initial CPU usage
+        initial_cpu = psutil.cpu_percent(interval=0.1)
+        
+        # CPU-intensive task
+        def cpu_intensive_task():
+            result = 0
+            for i in range(1000000):
+                result += i * i
+            return result
+        
+        # Run CPU-intensive task
+        start_time = time.time()
+        cpu_intensive_task()
+        end_time = time.time()
+        
+        # Get CPU usage during task
+        cpu_usage = psutil.cpu_percent(interval=0.1)
+        
+        # Performance assertions
+        execution_time = end_time - start_time
+        assert execution_time < 5.0  # Should complete in under 5 seconds
+        assert cpu_usage > 0        # Should show CPU usage
+    
+    def test_disk_io_performance(self):
+        """Test disk I/O performance"""
+        import tempfile
+        from pathlib import Path
+        
+        with tempfile.TemporaryDirectory() as temp_dir:
+            temp_path = Path(temp_dir)
+            
+            # Test write performance
+            test_data = 'x' * (1024 * 1024)  # 1MB of data
+            write_times = []
+            
+            for i in range(10):
+                file_path = temp_path / f"test_file_{i}.txt"
+                start_time = time.time()
+                
+                with open(file_path, 'w') as f:
+                    f.write(test_data)
+                
+                end_time = time.time()
+                write_times.append(end_time - start_time)
+            
+            # Test read performance
+            read_times = []
+            
+            for i in range(10):
+                file_path = temp_path / f"test_file_{i}.txt"
+                start_time = time.time()
+                
+                with open(file_path, 'r') as f:
+                    data = f.read()
+                
+                end_time = time.time()
+                read_times.append(end_time - start_time)
+                assert len(data) == len(test_data)
+            
+            # Performance analysis
+            avg_write_time = statistics.mean(write_times)
+            avg_read_time = statistics.mean(read_times)
+            
+            assert avg_write_time < 0.1  # Write should be under 100ms per MB
+            assert avg_read_time < 0.05   # Read should be under 50ms per MB
+    
+    def test_network_performance(self):
+        """Test network I/O performance"""
+        # Mock network operations
+        def mock_network_request(size_kb):
+            # Simulate network latency and bandwidth
+            latency = 0.01  # 10ms latency
+            bandwidth_time = size_kb / 1000  # 1MB/s bandwidth
+            total_time = latency + bandwidth_time
+            time.sleep(total_time)
+            return {'size': size_kb, 'time': total_time}
+        
+        # Test different request sizes
+        request_sizes = [10, 100, 1000]  # KB
+        network_results = []
+        
+        for size in request_sizes:
+            result = mock_network_request(size)
+            network_results.append(result)
+            
+            assert result['size'] == size
+            assert result['time'] > 0
+        
+        # Performance analysis
+        throughputs = [size / result['time'] for size, result in zip(request_sizes, network_results)]
+        avg_throughput = statistics.mean(throughputs)
+        
+        assert avg_throughput > 500  # Should achieve at least 500 KB/s
+
+
+class TestScalabilityMetrics:
+    """Test system scalability metrics"""
+    
+    def test_load_scaling(self):
+        """Test system behavior under increasing load"""
+        # Mock system under different loads
+        def mock_system_load(load_factor):
+            # Simulate increasing response times with load
+            base_response_time = 0.1
+            load_response_time = base_response_time * (1 + load_factor * 0.1)
+            time.sleep(load_response_time)
+            return {
+                'load_factor': load_factor,
+                'response_time': load_response_time,
+                'throughput': 1 / load_response_time
+            }
+        
+        # Test different load factors
+        load_factors = [1, 2, 5, 10]  # 1x, 2x, 5x, 10x load
+        scaling_results = []
+        
+        for load in load_factors:
+            result = mock_system_load(load)
+            scaling_results.append(result)
+            
+            assert result['load_factor'] == load
+            assert result['response_time'] > 0
+            assert result['throughput'] > 0
+        
+        # Scalability analysis
+        response_times = [r['response_time'] for r in scaling_results]
+        throughputs = [r['throughput'] for r in scaling_results]
+        
+        # Check that response times increase reasonably
+        assert response_times[-1] < response_times[0] * 5  # Should not be 5x slower at 10x load
+        
+        # Check that throughput degrades gracefully
+        assert throughputs[-1] > throughputs[0] / 5  # Should maintain at least 20% of peak throughput
+    
+    def test_resource_efficiency(self):
+        """Test resource efficiency metrics"""
+        # Mock resource usage
+        def mock_resource_usage(requests_per_second):
+            # Simulate resource usage scaling
+            cpu_usage = min(90, requests_per_second * 2)  # 2% CPU per request/sec
+            memory_usage = min(80, 50 + requests_per_second * 0.5)  # Base 50% + 0.5% per request/sec
+            return {
+                'requests_per_second': requests_per_second,
+                'cpu_usage': cpu_usage,
+                'memory_usage': memory_usage,
+                'efficiency': requests_per_second / max(cpu_usage, memory_usage)
+            }
+        
+        # Test different request rates
+        request_rates = [10, 25, 50, 100]  # requests per second
+        efficiency_results = []
+        
+        for rate in request_rates:
+            result = mock_resource_usage(rate)
+            efficiency_results.append(result)
+            
+            assert result['requests_per_second'] == rate
+            assert result['cpu_usage'] <= 100
+            assert result['memory_usage'] <= 100
+        
+        # Efficiency analysis
+        efficiencies = [r['efficiency'] for r in efficiency_results]
+        max_efficiency = max(efficiencies)
+        
+        assert max_efficiency > 1.0  # Should achieve reasonable efficiency