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 predicts future test execution load"""

        historical_data = pd.DataFrame({
            'timestamp': pd.date_range('2025-01-01', periods=90, freq='H'),
            'concurrent_tests': [...],
            'cpu_usage': [...],
            'memory_usage': [...],
            'day_of_week': [...],
            'is_release_week': [...]
        })

        self.scaler.train(historical_data)

        predictions = self.scaler.predict_load(
            forecast_hours=24,
            confidence_level=0.95
        )

        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"""

        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
        )

        current_load = {
            'active_tests': 45,
            'cpu_utilization': 0.68,
            'memory_utilization': 0.72,
            'queue_depth': 12
        }

        scaling_decision = self.scaler.evaluate_scaling(
            current_load=current_load,
            policy=policy
        )

        if scaling_decision.should_scale:
            print(f"Action: {scaling_decision.action}")
            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")

            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'
        )

        sla = {
            'max_test_duration_minutes': 30,
            'max_queue_wait_minutes': 5,
            'availability': 0.99
        }

        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}%")

        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'
        )

        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_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},
        ]

        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}")

        assert allocation_plan.total_cost < 5.0
        assert allocation_plan.total_duration < 65
        assert allocation_plan.resource_utilization > 0.70