Sentinel
The Quality Guardian
Sentinel serves as your autonomous quality guardian, creating and maintaining comprehensive testing strategies that evolve with your codebase. This intelligent testing specialist ensures code quality, identifies edge cases, and prevents regressions while adapting to changing requirements and system complexity.
Comprehensive Test Generation
Intelligent Test Creation: Sentinel automatically generates unit tests, integration tests, end-to-end scenarios, and performance tests based on code analysis and user behavior patterns. The agent understands code structure, dependencies, and potential failure points to create meaningful test coverage.
Edge Case Detection: Using advanced analysis techniques, Sentinel identifies edge cases and boundary conditions that human testers might overlook. This includes unusual input combinations, race conditions, error scenarios, and integration failure patterns.
Behavioral Testing: Tests reflect real user behavior patterns rather than just code coverage. Sentinel analyzes user interactions, system logs, and business requirements to create tests that validate actual usage scenarios.
Advanced Test Suite Architecture
# Sentinel-generated comprehensive test framework
import pytest
import asyncio
from unittest.mock import Mock, patch, AsyncMock
from datetime import datetime, timedelta
import json
from dataclasses import dataclass
from typing import List, Dict, Any, Optional
@dataclass
class TestScenario:
"""Sentinel-generated test scenario with metadata"""
name: str
description: str
category: str
risk_level: str
business_impact: str
expected_frequency: str
class SentinelTestFramework:
"""
Comprehensive testing framework generated by Sentinel.
Includes edge case detection, performance validation, and business logic testing.
"""
@pytest.fixture(scope="session")
def test_environment(self):
"""Setup isolated test environment with realistic data"""
return {
'database': self._create_test_database(),
'cache': self._create_test_cache(),
'external_services': self._setup_service_mocks(),
'user_sessions': self._generate_test_sessions()
}
@pytest.fixture
def payment_processor(self, test_environment):
"""Payment processor with test configuration"""
return PaymentProcessor(
config={
'database_url': test_environment['database']['url'],
'cache_url': test_environment['cache']['url'],
'timeout': 30,
'retry_attempts': 3,
'rate_limit': 1000,
'test_mode': True
}
)
class TestPaymentProcessingEdgeCases:
"""
Sentinel-identified edge cases for payment processing.
These scenarios are based on real-world failure patterns and user behavior analysis.
"""
@pytest.mark.parametrize("edge_case_scenario", [
TestScenario(
name="concurrent_payments_same_user",
description="Multiple simultaneous payments from same user",
category="concurrency",
risk_level="high",
business_impact="duplicate_charges",
expected_frequency="daily"
),
TestScenario(
name="payment_during_maintenance",
description="Payment attempt during system maintenance",
category="availability",
risk_level="medium",
business_impact="customer_frustration",
expected_frequency="monthly"
),
TestScenario(
name="extremely_large_payment",
description="Payment exceeding normal business limits",
category="business_logic",
risk_level="high",
business_impact="fraud_risk",
expected_frequency="rare"
),
TestScenario(
name="malformed_payment_data",
description="Invalid or corrupted payment information",
category="data_validation",
risk_level="medium",
business_impact="system_stability",
expected_frequency="weekly"
),
TestScenario(
name="gateway_cascade_failure",
description="Multiple payment gateways failing simultaneously",
category="infrastructure",
risk_level="critical",
business_impact="revenue_loss",
expected_frequency="yearly"
)
])
async def test_payment_edge_cases(self, payment_processor, edge_case_scenario, test_environment):
"""
Comprehensive edge case testing based on Sentinel analysis.
Each test scenario includes realistic failure simulation and recovery validation.
"""
if edge_case_scenario.name == "concurrent_payments_same_user":
await self._test_concurrent_payments(payment_processor, test_environment)
elif edge_case_scenario.name == "payment_during_maintenance":
await self._test_maintenance_mode_payment(payment_processor, test_environment)
elif edge_case_scenario.name == "extremely_large_payment":
await self._test_large_payment_handling(payment_processor, test_environment)
elif edge_case_scenario.name == "malformed_payment_data":
await self._test_malformed_data_handling(payment_processor, test_environment)
elif edge_case_scenario.name == "gateway_cascade_failure":
await self._test_gateway_cascade_failure(payment_processor, test_environment)
async def _test_concurrent_payments(self, processor, env):
"""Test handling of concurrent payment attempts from same user"""
user_id = "user_concurrent_test"
payment_data = {
'amount': 99.99,
'currency': 'USD',
'user_id': user_id,
'payment_method': 'credit_card'
}
# Launch 5 concurrent payment attempts
tasks = []
for i in range(5):
payment_copy = payment_data.copy()
payment_copy['idempotency_key'] = f"concurrent_test_{i}"
tasks.append(processor.process_payment(payment_copy))
results = await asyncio.gather(*tasks, return_exceptions=True)
# Verify idempotency - only one payment should succeed
successful_payments = [r for r in results if hasattr(r, 'success') and r.success]
assert len(successful_payments) == 1, "Multiple concurrent payments succeeded"
# Verify proper error handling for duplicates
duplicate_errors = [r for r in results if hasattr(r, 'error_code') and r.error_code == 'DUPLICATE_PAYMENT']
assert len(duplicate_errors) == 4, "Duplicate detection failed"
async def _test_gateway_cascade_failure(self, processor, env):
"""Test system behavior when all payment gateways fail"""
# Simulate all gateways failing
with patch.multiple(
'payment_gateways',
stripe_gateway=AsyncMock(side_effect=Exception("Service unavailable")),
paypal_gateway=AsyncMock(side_effect=Exception("Service unavailable")),
square_gateway=AsyncMock(side_effect=Exception("Service unavailable"))
):
payment_data = {
'amount': 149.99,
'currency': 'USD',
'user_id': 'user_cascade_test',
'payment_method': 'credit_card'
}
result = await processor.process_payment(payment_data)
# Should gracefully degrade to manual processing queue
assert result.status == 'queued_for_manual_processing'
assert result.estimated_processing_time is not None
assert 'temporary service disruption' in result.user_message.lower()
# Verify customer notification was sent
notifications = env['external_services']['notification_service'].call_history
assert any('payment delay' in str(call) for call in notifications)
class TestPerformanceValidation:
"""
Sentinel-generated performance tests based on system analysis and usage patterns.
"""
@pytest.mark.performance
async def test_payment_processing_under_load(self, payment_processor):
"""Validate payment processing performance under realistic load"""
# Generate realistic load pattern based on Sentinel analysis
payment_requests = self._generate_realistic_payment_load(1000)
start_time = datetime.utcnow()
# Process payments with controlled concurrency
semaphore = asyncio.Semaphore(50) # Max 50 concurrent requests
async def process_with_semaphore(payment_data):
async with semaphore:
return await payment_processor.process_payment(payment_data)
results = await asyncio.gather(
*[process_with_semaphore(payment) for payment in payment_requests],
return_exceptions=True
)
processing_time = (datetime.utcnow() - start_time).total_seconds()
# Performance assertions based on SLA requirements
assert processing_time < 120, f"Processing took {processing_time}s, expected < 120s"
successful_payments = [r for r in results if hasattr(r, 'success') and r.success]
success_rate = len(successful_payments) / len(payment_requests)
assert success_rate >= 0.99, f"Success rate {success_rate:.2%} below 99% threshold"
# Response time distribution analysis
response_times = [r.processing_time for r in successful_payments if hasattr(r, 'processing_time')]
avg_response_time = sum(response_times) / len(response_times)
p95_response_time = sorted(response_times)[int(len(response_times) * 0.95)]
assert avg_response_time < 2.0, f"Average response time {avg_response_time:.2f}s > 2.0s"
assert p95_response_time < 5.0, f"P95 response time {p95_response_time:.2f}s > 5.0s"
def _generate_realistic_payment_load(self, count: int) -> List[Dict[str, Any]]:
"""Generate realistic payment data based on actual usage patterns"""
import random
# Payment amount distribution based on real data analysis
amount_distribution = [
(0.4, lambda: round(random.uniform(5, 50), 2)), # Small purchases
(0.3, lambda: round(random.uniform(50, 200), 2)), # Medium purchases
(0.2, lambda: round(random.uniform(200, 1000), 2)), # Large purchases
(0.1, lambda: round(random.uniform(1000, 5000), 2)) # Premium purchases
]
payments = []
for i in range(count):
# Select amount based on distribution
rand = random.random()
cumulative = 0
for prob, amount_func in amount_distribution:
cumulative += prob
if rand <= cumulative:
amount = amount_func()
break
payments.append({
'amount': amount,
'currency': random.choice(['USD', 'EUR', 'GBP']),
'user_id': f'load_test_user_{i % 100}', # Simulate 100 different users
'payment_method': random.choice(['credit_card', 'debit_card', 'paypal']),
'idempotency_key': f'load_test_{i}_{datetime.utcnow().timestamp()}'
})
return payments
class TestBusinessLogicValidation:
"""
Sentinel-generated tests for business logic validation and compliance.
"""
@pytest.mark.business_logic
async def test_fraud_detection_integration(self, payment_processor):
"""Validate fraud detection triggers and responses"""
# Test suspicious payment patterns identified by Sentinel
suspicious_scenarios = [
{
'name': 'rapid_succession_payments',
'payments': [
{'amount': 999.99, 'user_id': 'user_fraud_test', 'delay': 0},
{'amount': 999.99, 'user_id': 'user_fraud_test', 'delay': 1},
{'amount': 999.99, 'user_id': 'user_fraud_test', 'delay': 2}
],
'expected_trigger': True
},
{
'name': 'unusual_amount_pattern',
'payments': [
{'amount': 9999.99, 'user_id': 'user_normal', 'delay': 0}
],
'expected_trigger': True
},
{
'name': 'normal_payment_pattern',
'payments': [
{'amount': 49.99, 'user_id': 'user_normal', 'delay': 0}
],
'expected_trigger': False
}
]
for scenario in suspicious_scenarios:
fraud_alerts = []
# Monitor fraud detection system
with patch('fraud_detection.alert_system.send_alert') as mock_alert:
mock_alert.side_effect = lambda alert: fraud_alerts.append(alert)
# Execute payment scenario
for payment_config in scenario['payments']:
if payment_config['delay'] > 0:
await asyncio.sleep(payment_config['delay'])
payment_data = {
'amount': payment_config['amount'],
'currency': 'USD',
'user_id': payment_config['user_id'],
'payment_method': 'credit_card'
}
result = await payment_processor.process_payment(payment_data)
if scenario['expected_trigger']:
assert result.fraud_check_status in ['flagged', 'blocked']
else:
assert result.fraud_check_status == 'passed'
# Verify fraud detection triggered appropriately
if scenario['expected_trigger']:
assert len(fraud_alerts) > 0, f"Fraud detection failed for {scenario['name']}"
else:
assert len(fraud_alerts) == 0, f"False positive fraud detection for {scenario['name']}"Adaptive Test Strategies
Risk-Based Testing: Sentinel adapts testing strategies based on code complexity, risk assessment, and historical failure patterns. Critical components receive more comprehensive testing, while stable areas maintain appropriate but efficient coverage.
Continuous Test Evolution: As your codebase evolves, Sentinel automatically updates test suites to maintain coverage and relevance. When new features are added or existing functionality changes, corresponding tests are generated or modified automatically.
Performance Test Integration: The agent automatically generates performance tests that validate system behavior under various load conditions. These tests identify performance regressions, scaling bottlenecks, and resource utilization issues before they impact production.
Security Testing Integration
Vulnerability Testing: Working in coordination with Aegis, Sentinel incorporates security testing into automated test suites. This includes input validation testing, authentication verification, authorization checks, and integration with security scanning tools.
Compliance Validation: Tests automatically verify compliance with industry standards and regulatory requirements. Sentinel ensures that security controls, data handling procedures, and audit requirements are validated through automated testing.
Test Data Management
Realistic Data Generation: Sentinel generates test data that reflects production scenarios while protecting sensitive information. The agent creates data sets that cover typical usage patterns, edge cases, and stress conditions without exposing real user data.
Data Privacy Protection: All test data generation respects privacy requirements and regulatory constraints. Sensitive information is properly anonymized or synthetically generated to ensure compliance while maintaining test realism.
Failure Analysis and Learning
Root Cause Analysis: When tests fail, Sentinel provides detailed analysis including root cause identification, impact assessment, and suggested remediation strategies. This accelerates debugging and reduces time to resolution.
Pattern Recognition: The agent learns from test failures and system behavior to improve future test generation. Patterns that lead to issues are incorporated into test scenarios to prevent regression.
Continuous Improvement: Test effectiveness is continuously monitored and optimized. Sentinel analyzes test results, execution times, and coverage metrics to improve test suite efficiency and effectiveness.
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