AI Test Infrastructure: Smart Resource Management

from ai_infrastructure import PredictiveScaler
import pandas as pd

class TestPredictiveScaling:
    def setup_method(self):
        self.scaler = PredictiveScaler(
            provider='aws',
            model='test-load-predictor-v2'
        )

    def test_predict_test_load(self):
        """AI (as discussed in [AI-powered Test Generation: The Future Is Already Here](/blog/ai-powered-test-generation)) predicts future test execution load"""

        # Historical test execution data
        historical_data = pd.DataFrame({
            'timestamp': pd.date_range('2025-01-01', periods=90, freq='H'),
            'concurrent_tests': [/* test counts */],
            'cpu_usage': [/* cpu metrics */],
            'memory_usage': [/* memory metrics */],
            'day_of_week': [/* weekday */],
            'is_release_week': [/* boolean */]
        })

        # Train (as discussed in [AI Test Metrics Analytics: Intelligent Analysis of QA Metrics](/blog/ai-test-metrics)) on historical patterns
        self.scaler.train(historical_data)

        # Predict next 24 hours
        predictions = self.scaler.predict_load(
            forecast_hours=24,
            confidence_level=0.95
        )

        # AI identifies peak load periods
        peak_hours = predictions[predictions.load > predictions.load.mean() + predictions.load.std()]

        print("Predicted Peak Load Periods:")
        for _, peak in peak_hours.iterrows():
            print(f"Time: {peak.timestamp}")
            print(f"Expected concurrent tests: {peak.concurrent_tests}")
            print(f"Required instances: {peak.recommended_instances}")
            print(f"Confidence: {peak.confidence}")

        assert len(predictions) == 24
        assert all(predictions.confidence > 0.85)

    def test_auto_scaling_execution(self):
        """AI automatically scales infrastructure based on predictions"""

        # Configure auto-scaling policy
        policy = self.scaler.create_scaling_policy(
            min_instances=2,
            max_instances=50,
            target_utilization=0.75,
            scale_up_threshold=0.80,
            scale_down_threshold=0.30,
            prediction_horizon_minutes=30
        )

        # Simulate test load increase
        current_load = {
            'active_tests': 45,
            'cpu_utilization': 0.68,
            'memory_utilization': 0.72,
            'queue_depth': 12
        }

        # AI decides scaling action
        scaling_decision = self.scaler.evaluate_scaling(
            current_load=current_load,
            policy=policy
        )

        if scaling_decision.should_scale:
            print(f"Action: {scaling_decision.action}")  # scale_up
            print(f"Current instances: {scaling_decision.current_instances}")
            print(f"Target instances: {scaling_decision.target_instances}")
            print(f"Reasoning: {scaling_decision.reasoning}")
            print(f"Expected cost impact: ${scaling_decision.cost_delta}/hour")

            # AI prevents overscaling
            assert scaling_decision.target_instances <= policy.max_instances
            assert scaling_decision.target_instances >= policy.min_instances

from ai_infrastructure import CostOptimizer

class TestCostOptimization:
    def test_minimize_cost_while_meeting_sla(self):
        """AI optimizes for cost while meeting performance SLAs"""

        optimizer = CostOptimizer(
            provider='aws',
            region='us-east-1'
        )

        # Define SLA requirements
        sla = {
            'max_test_duration_minutes': 30,
            'max_queue_wait_minutes': 5,
            'availability': 0.99
        }

        # AI finds optimal instance mix
        recommendation = optimizer.optimize_instance_mix(
            expected_load={
                'cpu_intensive_tests': 100,
                'memory_intensive_tests': 50,
                'io_intensive_tests': 30,
                'gpu_tests': 10
            },
            sla_requirements=sla,
            optimization_goal='minimize_cost'
        )

        print("Optimized Infrastructure:")
        for instance_type, count in recommendation.instance_mix.items():
            print(f"{instance_type}: {count} instances")
            print(f"  Cost/hour: ${recommendation.cost_per_hour[instance_type]}")

        print(f"\nTotal monthly cost: ${recommendation.monthly_cost}")
        print(f"SLA compliance: {recommendation.sla_compliance_score}")
        print(f"Cost savings vs baseline: {recommendation.savings_percentage}%")

        # Verify SLA is met
        assert recommendation.sla_compliance_score >= 0.99
        assert recommendation.max_test_duration <= 30

from ai_infrastructure import ResourceMatcher

class TestSmartAllocation:
    def test_intelligent_test_routing(self):
        """AI routes tests to optimal execution environments"""

        matcher = ResourceMatcher(
            model='test-resource-matcher-v3'
        )

        # Define test characteristics
        test_suite = [
            {'name': 'api_tests', 'cpu': 'medium', 'memory': 'low', 'duration': '5min'},
            {'name': 'ui_tests', 'cpu': 'high', 'memory': 'high', 'duration': '20min'},
            {'name': 'integration_tests', 'cpu': 'low', 'memory': 'medium', 'duration': '15min'},
            {'name': 'load_tests', 'cpu': 'very_high', 'memory': 'very_high', 'duration': '60min'},
        ]

        # Available infrastructure
        available_resources = [
            {'id': 'pool-a', 'type': 't3.medium', 'available': 10, 'cost_per_hour': 0.05},
            {'id': 'pool-b', 'type': 'c5.large', 'available': 5, 'cost_per_hour': 0.09},
            {'id': 'pool-c', 'type': 'm5.2xlarge', 'available': 2, 'cost_per_hour': 0.38},
        ]

        # AI creates optimal allocation plan
        allocation_plan = matcher.create_allocation_plan(
            tests=test_suite,
            resources=available_resources,
            optimization_criteria=['execution_time', 'cost', 'resource_efficiency']
        )

        for allocation in allocation_plan.allocations:
            print(f"Test: {allocation.test_name}")
            print(f"  Assigned to: {allocation.resource_pool}")
            print(f"  Expected duration: {allocation.estimated_duration}")
            print(f"  Cost: ${allocation.estimated_cost}")
            print(f"  Efficiency score: {allocation.efficiency_score}")

        # AI minimizes total cost and execution time
        assert allocation_plan.total_cost < 5.0  # Budget constraint
        assert allocation_plan.total_duration < 65  # Parallel execution
        assert allocation_plan.resource_utilization > 0.70  # Efficient use

    def test_dynamic_reallocation(self):
        """AI dynamically reallocates tests when resources become available"""

        matcher = ResourceMatcher()

        # Initial allocation
        initial_plan = matcher.create_allocation_plan(tests, resources)

        # Simulate resource becoming available mid-execution
        matcher.notify_resource_available(
            resource_id='pool-d',
            resource_type='c5.4xlarge',
            available_at='2025-10-04T14:30:00Z'
        )

        # AI reoptimizes allocation
        updated_plan = matcher.reoptimize_allocation(
            current_plan=initial_plan,
            current_time='2025-10-04T14:25:00Z'
        )

        # Should migrate long-running tests to more powerful resources
        migrations = updated_plan.get_migrations()
        assert len(migrations) > 0

        for migration in migrations:
            print(f"Migrating {migration.test_name}")
            print(f"  From: {migration.current_resource}")
            print(f"  To: {migration.target_resource}")
            print(f"  Time saved: {migration.time_savings} minutes")

from ai_infrastructure import DataProvisioner

class TestDataManagement:
    def test_predict_data_requirements(self):
        """AI predicts test data requirements"""

        provisioner = DataProvisioner(
            model='test-data-predictor'
        )

        # Analyze test suite data needs
        test_suite_metadata = {
            'total_tests': 500,
            'test_categories': ['api', 'ui', 'integration'],
            'data_dependencies': load_data_dependency_graph()
        }

        # AI predicts data requirements
        data_plan = provisioner.predict_data_requirements(
            test_suite=test_suite_metadata,
            execution_parallelism=10
        )

        print("Data Provisioning Plan:")
        print(f"Total datasets required: {data_plan.dataset_count}")
        print(f"Total data volume: {data_plan.total_size_gb} GB")
        print(f"Provisioning time estimate: {data_plan.provisioning_time_minutes} minutes")

        # AI optimizes data sharing
        print("\nData Sharing Opportunities:")
        for sharing in data_plan.sharing_opportunities:
            print(f"Dataset: {sharing.dataset_name}")
            print(f"  Shared by {len(sharing.tests)} tests")
            print(f"  Storage savings: {sharing.storage_savings_gb} GB")

    def test_synthetic_data_generation(self):
        """AI generates synthetic test data"""

        provisioner = DataProvisioner()

        # Define data schema
        schema = {
            'users': {
                'fields': ['id', 'name', 'email', 'age', 'country'],
                'constraints': {
                    'age': {'min': 18, 'max': 80},
                    'country': {'values': ['US', 'UK', 'DE', 'FR', 'JP']}
                }
            }
        }

        # AI generates realistic synthetic data
        synthetic_data = provisioner.generate_synthetic_data(
            schema=schema,
            record_count=10000,
            quality='production_like',
            privacy_safe=True
        )

        # Verify data quality
        assert len(synthetic_data['users']) == 10000
        assert all(18 <= user['age'] <= 80 for user in synthetic_data['users'])
        assert provisioner.validate_privacy_compliance(synthetic_data) is True

        # AI ensures data distribution matches production
        distribution_score = provisioner.compare_distributions(
            synthetic_data=synthetic_data,
            production_sample=load_production_sample()
        )
        assert distribution_score > 0.90  # 90% similarity

from ai_infrastructure import DriftDetector

class TestDriftDetection:
    def test_detect_environment_drift(self):
        """AI detects configuration drift across environments"""

        detector = DriftDetector()

        # Scan environments
        environments = {
            'dev': detector.scan_environment('dev'),
            'staging': detector.scan_environment('staging'),
            'prod': detector.scan_environment('prod')
        }

        # AI identifies drift
        drift_analysis = detector.analyze_drift(
            baseline='prod',
            targets=['dev', 'staging']
        )

        print("Configuration Drift Detected:")
        for drift in drift_analysis.critical_drifts:
            print(f"Component: {drift.component}")
            print(f"Environments: {drift.environments}")
            print(f"Difference: {drift.difference}")
            print(f"Impact: {drift.impact_assessment}")
            print(f"Remediation: {drift.suggested_fix}")

        # AI auto-remediation
        if drift_analysis.auto_fixable_count > 0:
            remediation_plan = drift_analysis.create_remediation_plan()
            assert len(remediation_plan.steps) > 0

from ai_infrastructure import IaCGenerator

class TestIaCGeneration:
    def test_generate_terraform_from_requirements(self):
        """AI generates Terraform configuration from requirements"""

        generator = IaCGenerator(
            provider='aws',
            format='terraform'
        )

        requirements = {
            'test_execution_capacity': {
                'concurrent_tests': 100,
                'peak_load_multiplier': 2,
                'test_types': ['api', 'ui', 'integration']
            },
            'data_requirements': {
                'databases': ['postgresql', 'redis'],
                'storage_gb': 500
            },
            'network': {
                'isolation': 'vpc_per_environment',
                'external_access': False
            },
            'budget_constraints': {
                'max_monthly_cost': 5000
            }
        }

        # AI generates optimal IaC
        iac_config = generator.generate_infrastructure(requirements)

        print("Generated Terraform:")
        print(iac_config.terraform_code)

        # AI includes best practices
        assert 'autoscaling' in iac_config.terraform_code
        assert 'lifecycle' in iac_config.terraform_code

        # Verify cost estimation
        cost_estimate = iac_config.estimate_monthly_cost()
        assert cost_estimate < requirements['budget_constraints']['max_monthly_cost']

from ai_infrastructure import InfrastructureMonitor

class TestAnomalyDetection:
    def test_detect_infrastructure_anomalies(self):
        """AI detects unusual infrastructure behavior"""

        monitor = InfrastructureMonitor(
            model='anomaly-detector-v2'
        )

        # Feed real-time metrics
        metrics_stream = load_metrics_stream()

        anomalies = monitor.detect_anomalies(
            metrics=metrics_stream,
            sensitivity='high'
        )

        for anomaly in anomalies:
            print(f"Anomaly: {anomaly.type}")
            print(f"Severity: {anomaly.severity}")
            print(f"Affected resource: {anomaly.resource}")
            print(f"Root cause: {anomaly.predicted_root_cause}")
            print(f"Recommended action: {anomaly.remediation}")

        # AI predicts failures before they occur
        predictions = monitor.predict_failures(
            lookahead_minutes=60
        )

        assert len(predictions) >= 0