Infraestructura de Pruebas con IA: Gestión Inteligente de Recursos

from ai_infrastructure import PredictiveScaler
import pandas as pd

class TestPredictiveScaling:
    def setup_method(self):
        self.scaler = PredictiveScaler(
            provider='aws' (como se discute en [AI Copilot for Test Automation: GitHub Copilot, Amazon CodeWhisperer and the Future of QA](/blog/ai-copilot-testing)),
            model='test-load-predictor-v2'
        )

    def test_predict_test_load(self):
        """IA predice carga futura de ejecución de pruebas"""

        # Datos históricos de ejecución de pruebas
        historical_data = pd.DataFrame({
            'timestamp': pd.date_range('2025-01-01', periods=90, freq='H'),
            'concurrent_tests': [/* conteos de pruebas */],
            'cpu_usage': [/* métricas cpu */],
            'memory_usage': [/* métricas memoria */],
            'day_of_week': [/* día semana */],
            'is_release_week': [/* booleano */]
        })

        # Entrenar en patrones históricos
        self.scaler.train(historical_data)

 (como se discute en [AI-powered Test Generation: The Future Is Already Here](/blog/ai-powered-test-generation))        # Predecir próximas 24 horas
        predictions = self.scaler.predict_load(
            forecast_hours=24,
            confidence_level=0.95
        )

        # IA identifica períodos de carga pico
        peak_hours = predictions[predictions.load > predictions.load.mean() + predictions.load.std()]

        print("Períodos de Carga Pico Predichos:")
        for _, peak in peak_hours.iterrows():
            print(f"Hora: {peak.timestamp}")
            print(f"Pruebas concurrentes esperadas: {peak.concurrent_tests}")
            print(f"Instancias requeridas: {peak.recommended_instances}")
            print(f"Confianza: {peak.confidence}")

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

from ai_infrastructure import CostOptimizer

class TestCostOptimization:
    def test_minimize_cost_while_meeting_sla(self):
        """IA optimiza para costo mientras cumple SLAs de rendimiento"""

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

        # Definir requisitos SLA
        sla = {
            'max_test_duration_minutes': 30,
            'max_queue_wait_minutes': 5,
            'availability': 0.99
        }

        # IA encuentra mezcla óptima de instancias
        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("Infraestructura Optimizada:")
        for instance_type, count in recommendation.instance_mix.items():
            print(f"{instance_type}: {count} instancias")
            print(f"  Costo/hora: ${recommendation.cost_per_hour[instance_type]}")

        print(f"\nCosto mensual total: ${recommendation.monthly_cost}")
        print(f"Cumplimiento SLA: {recommendation.sla_compliance_score}")
        print(f"Ahorro vs línea base: {recommendation.savings_percentage}%")

        assert recommendation.sla_compliance_score >= 0.99

from ai_infrastructure import ResourceMatcher

class TestSmartAllocation:
    def test_intelligent_test_routing(self):
        """IA enruta pruebas a entornos de ejecución óptimos"""

        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': 'load_tests', 'cpu': 'very_high', 'memory': 'very_high', 'duration': '60min'},
        ]

        available_resources = [
            {'id': 'pool-a', 'type': 't3.medium', 'available' (como se discute en [AI Test Metrics Analytics: Intelligent Analysis of QA Metrics](/blog/ai-test-metrics)): 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},
        ]

        # IA crea plan de asignación óptimo
        allocation_plan = matcher.create_allocation_plan(
            tests=test_suite,
            resources=available_resources,
            optimization_criteria=['execution_time', 'cost', 'resource_efficiency']
        )

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