# E2E Testing Tools and Automation Framework

**Document Version**: 1.0
**Date**: January 2025
**Status**: Implementation Ready
**Author**: Kubernaut Development Team

---

## Executive Summary

This document outlines the comprehensive testing tools and automation framework designed to support the **Top 10 E2E Use Cases** for Kubernaut. The framework leverages existing development infrastructure while adding specialized tools for chaos engineering, business outcome validation, and AI model stress testing.

### Framework Objectives

- **Accelerate Testing Development**: Reduce test implementation time by 60%
- **Ensure Business Outcome Validation**: Automated BR-XXX requirement validation
- **Minimize Maintenance Overhead**: Self-healing test infrastructure
- **Enable Continuous Validation**: 24/7 automated testing and monitoring
- **Support Chaos Engineering**: Controlled failure injection and recovery validation
- **Container-Native Integration**: Seamless integration with HolmesGPT REST API container

---

## 🛠️ **Core Testing Tools**

### **1. Enhanced Test Factory (`KubernautE2ETestFactory`)**

#### **Architecture Overview**
Extends the existing `test/integration/shared/test_factory.go` with e2e-specific capabilities while maintaining compatibility with current test patterns.

```go
// E2ETestSuite extends StandardTestSuite with chaos engineering and business validation
type E2ETestSuite struct {
    *shared.StandardTestSuite

    // Chaos Engineering Components
    ChaosEngine     *litmus.ChaosEngine
    ChaosValidator  *ChaosResultValidator

    // Load Testing Components
    LoadGenerator   *LoadTestingEngine
    StressTestSuite *AIModelStressTest

    // Business Validation Components
    MetricsValidator *BusinessMetricsValidator
    SLAMonitor      *ServiceLevelMonitor

    // Integration Components
    HolmesGPTClient *holmesgpt.RESTAPIClient  // HTTP client for container API
    ContextAPIClient *contextapi.TestClient

    // Test Management
    TestOrchestrator *E2ETestOrchestrator
    ResultAggregator *BusinessOutcomeAggregator
}
```

#### **Key Features**
- **Business Requirement Traceability**: Automatic BR-XXX requirement validation
- **Chaos Integration**: Seamless LitmusChaos experiment orchestration
- **AI Model Testing**: Specialized oss-gpt:20b stress testing capabilities
- **Real-time Monitoring**: Live SLA and performance tracking
- **Automated Recovery Validation**: Post-chaos system health verification

#### **Implementation Pattern**
```go
// Example usage following existing patterns
func setupE2ETestSuite(suiteName string) *E2ETestSuite {
    // Reuse existing StandardTestSuite configuration
    baseConfig := shared.NewStandardTestSuite(suiteName,
        shared.WithRealDatabase(),
        shared.WithRealLLM(),
        shared.WithRealVectorDB(),
    )

    // Extend with e2e capabilities
    return &E2ETestSuite{
        StandardTestSuite: baseConfig,
        ChaosEngine:      litmus.NewChaosEngine(baseConfig.TestEnv),
        LoadGenerator:    NewLoadTestingEngine(),
        MetricsValidator: NewBusinessMetricsValidator(),
        // ... additional components
    }
}
```

---

### **2. Chaos Engineering Automation**

#### **LitmusChaos Orchestrator**
Automated experiment scheduling, execution, and result validation integrated with Ginkgo/Gomega test framework.

```yaml
# Chaos Experiment Configuration Template
apiVersion: litmuschaos.io/v1alpha1
kind: ChaosEngine
metadata:
  name: kubernaut-e2e-chaos-{{.TestCase}}
  namespace: chaos-testing
spec:
  engineState: 'active'
  chaosServiceAccount: chaos-e2e-runner
  experiments:
  - name: "{{.ChaosType}}"
    spec:
      components:
        statusCheckTimeouts:
          delay: 2
          timeout: 180
        probe:
        - name: "kubernaut-health-check"
          type: "httpProbe"
          mode: "Continuous"
          runProperties:
            probeTimeout: 5
            interval: 2
            retry: 3
          httpProbe/inputs:
            url: "http://kubernaut-service:8080/health"
            expectedResponseCodes: ["200"]
        - name: "business-outcome-validation"
          type: "k8sProbe"
          mode: "EOT"
          k8sProbe/inputs:
            group: ""
            version: "v1"
            resource: "pods"
            namespace: "kubernaut-system"
            fieldSelector: "status.phase=Running"
```

#### **Custom Chaos Experiments**
Kubernaut-specific failure scenarios that align with business requirements.

```go
// Custom chaos experiments for AI model testing
type AIModelChaosExperiment struct {
    ModelEndpoint    string `yaml:"model_endpoint"`
    ConcurrentLoad   int    `yaml:"concurrent_load"`
    DurationMinutes  int    `yaml:"duration_minutes"`
    ExpectedFallback bool   `yaml:"expected_fallback"`
}

// Business requirement validation during chaos
type BusinessRequirementValidator struct {
    RequirementID    string                 `yaml:"requirement_id"`
    SuccessCriteria  map[string]interface{} `yaml:"success_criteria"`
    ValidationProbes []ValidationProbe      `yaml:"validation_probes"`
}
```

#### **Chaos Result Validator**
Automated business outcome validation post-chaos with detailed reporting.

```go
type ChaosValidationResult struct {
    ExperimentName     string                    `json:"experiment_name"`
    BusinessRequirement string                   `json:"business_requirement"`
    TestOutcome        BusinessOutcome          `json:"test_outcome"`
    PerformanceMetrics ChaosPerformanceMetrics  `json:"performance_metrics"`
    RecoveryValidation RecoveryValidationResult `json:"recovery_validation"`
    AIDecisionQuality  AIDecisionMetrics        `json:"ai_decision_quality"`
}

type BusinessOutcome struct {
    RequirementsMet    []string  `json:"requirements_met"`
    RequirementsFailed []string  `json:"requirements_failed"`
    OverallSuccess     bool      `json:"overall_success"`
    BusinessImpact     float64   `json:"business_impact_score"`
}
```

---

### **3. AI Model Stress Testing Framework**

#### **AI Stress Test Engine**
Specialized testing for oss-gpt:20b model performance under various load conditions.

```bash
#!/bin/bash
# AI Model Stress Testing Script (extends existing validation scripts)
# Builds on scripts/validate-localai-integration.sh

./scripts/e2e-ai-stress-test.sh \
  --model-endpoint localhost:8080 \
  --model gpt-oss:20b \
  --test-scenario pod-memory-pressure \
  --concurrent-requests 100 \
  --duration 30m \
  --business-requirement BR-AI-001 \
  --expected-accuracy 90% \
  --timeout-threshold 30s \
  --fallback-validation enabled
```

#### **AI Decision Quality Metrics**
Real-time validation of AI decision-making quality during stress conditions.

```go
type AIDecisionMetrics struct {
    DecisionAccuracy        float64       `json:"decision_accuracy"`
    ResponseTime           time.Duration `json:"response_time"`
    TimeoutRate            float64       `json:"timeout_rate"`
    FallbackActivations    int          `json:"fallback_activations"`
    BusinessRequirementMet bool         `json:"business_requirement_met"`

    // Pattern recognition metrics
    PatternRecognitionRate float64       `json:"pattern_recognition_rate"`
    ContextUnderstanding   float64       `json:"context_understanding"`
    ActionRelevance        float64       `json:"action_relevance"`
}
```

#### **Model Performance Validation**
Automated validation of model performance against business requirements.

```go
// AI Model Performance Test Suite
var _ = Describe("AI Model Stress Testing", func() {
    Context("BR-AI-001: Intelligent Alert Analysis", func() {
        It("should maintain 90% accuracy under 100 concurrent requests", func() {
            // Test configuration
            stressConfig := &AIStressTestConfig{
                ConcurrentRequests: 100,
                Duration:          30 * time.Minute,
                ModelEndpoint:     "localhost:8080",
                Model:             "gpt-oss:20b",
                AccuracyThreshold: 0.90,
            }

            // Execute stress test
            result, err := aiStressTestEngine.ExecuteStressTest(ctx, stressConfig)
            Expect(err).ToNot(HaveOccurred())

            // Validate business requirements
            Expect(result.DecisionAccuracy).To(BeNumerically(">=", 0.90))
            Expect(result.BusinessRequirementMet).To(BeTrue())
            Expect(result.TimeoutRate).To(BeNumerically("<", 0.05))
        })
    })
})
```

---

### **4. Business Metrics Validation Engine**

#### **Real-time SLA Monitoring**
Continuous monitoring of business requirements during test execution.

```go
type ServiceLevelMonitor struct {
    Requirements map[string]BusinessRequirement `json:"requirements"`
    Metrics      map[string]MetricCollector     `json:"metrics"`
    Alerts       []SLAViolationAlert           `json:"alerts"`
    Dashboard    *GrafanaDashboard             `json:"dashboard"`
}

type BusinessRequirement struct {
    ID               string            `json:"id"`
    Description      string            `json:"description"`
    SuccessCriteria  map[string]float64 `json:"success_criteria"`
    MonitoringProbes []MonitoringProbe  `json:"monitoring_probes"`
    AlertThresholds  AlertThresholds    `json:"alert_thresholds"`
}
```

#### **Performance Regression Detection**
Automated baseline comparison with historical performance data.

```go
type PerformanceBaseline struct {
    TestCase         string                 `json:"test_case"`
    BaselineMetrics  map[string]float64     `json:"baseline_metrics"`
    RegressionAlerts []RegressionAlert      `json:"regression_alerts"`
    TrendAnalysis    PerformanceTrendData   `json:"trend_analysis"`
}

// Regression detection logic
func (v *BusinessMetricsValidator) DetectRegression(
    current PerformanceMetrics,
    baseline PerformanceBaseline,
) (*RegressionReport, error) {
    report := &RegressionReport{
        TestTimestamp: time.Now(),
        Regressions:   []PerformanceRegression{},
    }

    for metric, currentValue := range current.Metrics {
        if baselineValue, exists := baseline.BaselineMetrics[metric]; exists {
            if regression := v.checkRegression(metric, currentValue, baselineValue); regression != nil {
                report.Regressions = append(report.Regressions, *regression)
            }
        }
    }

    return report, nil
}
```

#### **Quality Gates**
Automated pass/fail criteria based on business requirements.

```yaml
# Quality Gates Configuration
quality_gates:
  BR-AI-001:
    accuracy_threshold: 0.90
    response_time_max: "30s"
    availability_min: 0.999

  BR-SAFETY-001:
    zero_data_loss: true
    recovery_time_max: "5m"
    fallback_activation_max: "5s"

  BR-MONITOR-002:
    noise_reduction_min: 0.90
    alert_correlation_rate: 0.95
    processing_capacity_min: 1000 # alerts per minute
```

---

### **5. Integration Pipeline Automation**

#### **E2E Test Matrix Configuration**
Comprehensive test execution across multiple dimensions.

```yaml
# E2E Test Matrix Definition
e2e_test_matrix:
  chaos_scenarios:
    - name: "pod-failure"
      experiments: ["pod-delete", "pod-kill", "pod-failure"]
      business_requirements: ["BR-AI-001", "BR-SAFETY-001"]

    - name: "node-failure"
      experiments: ["node-drain", "node-taint", "kubelet-service-kill"]
      business_requirements: ["BR-AI-002", "BR-WF-001"]

    - name: "network-partition"
      experiments: ["pod-network-delay", "pod-network-loss", "dns-chaos"]
      business_requirements: ["BR-WF-002", "BR-MONITOR-001"]

    - name: "storage-corruption"
      experiments: ["disk-fill", "disk-loss"]
      business_requirements: ["BR-VDB-001", "BR-STORAGE-001"]

  ai_load_levels:
    - name: "normal"
      concurrent_requests: 10
      duration: "5m"

    - name: "high"
      concurrent_requests: 50
      duration: "15m"

    - name: "extreme"
      concurrent_requests: 100
      duration: "30m"

  cluster_configurations:
    - name: "small"
      nodes: 3
      resources: "minimal"

    - name: "medium"
      nodes: 6
      resources: "standard"

    - name: "large"
      nodes: 12
      resources: "high"

  validation_criteria: "business_requirements"
  reporting: "comprehensive"
```

#### **Automated Test Orchestration**
Intelligent test scheduling and execution management.

```go
type E2ETestOrchestrator struct {
    TestMatrix       TestMatrix                `json:"test_matrix"`
    ExecutionPlan    TestExecutionPlan         `json:"execution_plan"`
    ResourceManager  ClusterResourceManager    `json:"resource_manager"`
    ResultCollector  TestResultCollector       `json:"result_collector"`
}

// Intelligent test execution with resource optimization
func (o *E2ETestOrchestrator) ExecuteTestPlan(ctx context.Context) (*TestExecutionResult, error) {
    plan := o.generateOptimalExecutionPlan()

    results := &TestExecutionResult{
        ExecutionID:      uuid.New().String(),
        StartTime:       time.Now(),
        TestCases:       []TestCaseResult{},
        BusinessMetrics: BusinessOutcomeMetrics{},
    }

    for _, testBatch := range plan.TestBatches {
        batchResult, err := o.executeBatch(ctx, testBatch)
        if err != nil {
            return nil, fmt.Errorf("failed to execute test batch: %w", err)
        }
        results.TestCases = append(results.TestCases, batchResult.TestCases...)
    }

    results.EndTime = time.Now()
    results.Duration = results.EndTime.Sub(results.StartTime)

    return results, nil
}
```

---

### **6. Test Data Management**

#### **Realistic Alert Generator**
Production-like alert patterns for comprehensive testing.

```go
type RealisticAlertGenerator struct {
    AlertPatterns    []AlertPattern           `json:"alert_patterns"`
    ScenarioTemplates map[string]AlertScenario `json:"scenario_templates"`
    RandomSeed       int64                    `json:"random_seed"`
}

type AlertPattern struct {
    AlertType     string                 `json:"alert_type"`
    Frequency     time.Duration          `json:"frequency"`
    Severity      string                 `json:"severity"`
    Metadata      map[string]interface{} `json:"metadata"`
    Correlations  []string               `json:"correlations"`
}

// Generate realistic alert scenarios
func (g *RealisticAlertGenerator) GenerateAlertScenario(
    scenarioType string,
    duration time.Duration,
) (*AlertScenario, error) {
    template, exists := g.ScenarioTemplates[scenarioType]
    if !exists {
        return nil, fmt.Errorf("unknown scenario type: %s", scenarioType)
    }

    scenario := &AlertScenario{
        ScenarioID:   uuid.New().String(),
        ScenarioType: scenarioType,
        Duration:     duration,
        Alerts:       []Alert{},
    }

    // Generate alerts based on realistic patterns
    alertCount := g.calculateAlertCount(template, duration)
    for i := 0; i < alertCount; i++ {
        alert := g.generateAlert(template.Patterns[i%len(template.Patterns)])
        scenario.Alerts = append(scenario.Alerts, alert)
    }

    return scenario, nil
}
```

#### **Historical Pattern Injection**
Pre-populate vector database with realistic patterns for testing.

```go
type HistoricalPatternInjector struct {
    VectorDB        vector.VectorDatabase     `json:"vector_db"`
    PatternLibrary  []HistoricalPattern      `json:"pattern_library"`
    InjectionConfig PatternInjectionConfig   `json:"injection_config"`
}

type HistoricalPattern struct {
    PatternID      string                 `json:"pattern_id"`
    AlertType      string                 `json:"alert_type"`
    ActionHistory  []ActionExecution      `json:"action_history"`
    SuccessRate    float64               `json:"success_rate"`
    Embedding      []float64             `json:"embedding"`
    Metadata       map[string]interface{} `json:"metadata"`
}

// Inject realistic patterns for AI learning validation
func (h *HistoricalPatternInjector) InjectPatterns(ctx context.Context) error {
    for _, pattern := range h.PatternLibrary {
        // Store pattern in vector database
        err := h.VectorDB.StoreEmbedding(ctx, &vector.EmbeddingRecord{
            ID:        pattern.PatternID,
            Embedding: pattern.Embedding,
            Metadata:  pattern.Metadata,
        })
        if err != nil {
            return fmt.Errorf("failed to inject pattern %s: %w", pattern.PatternID, err)
        }
    }

    return nil
}
```

---

### **7. Observability & Validation Framework**

#### **Real-time Test Dashboards**
Grafana dashboards for comprehensive e2e test monitoring.

```yaml
# Grafana Dashboard Configuration
grafana_dashboards:
  e2e_test_overview:
    title: "Kubernaut E2E Test Overview"
    panels:
      - title: "Test Execution Status"
        type: "stat"
        targets:
          - expr: "sum(kubernaut_e2e_test_status)"
            legend: "{{test_case}}"

      - title: "Business Requirement Compliance"
        type: "table"
        targets:
          - expr: "kubernaut_business_requirement_status"
            legend: "{{requirement_id}}"

      - title: "AI Decision Accuracy"
        type: "graph"
        targets:
          - expr: "kubernaut_ai_decision_accuracy"
            legend: "Decision Accuracy %"

      - title: "Chaos Recovery Times"
        type: "heatmap"
        targets:
          - expr: "kubernaut_chaos_recovery_duration"
            legend: "{{chaos_type}}"

  business_kpi_tracking:
    title: "Business KPI Tracking"
    panels:
      - title: "Incident Response Time"
        type: "graph"
        targets:
          - expr: "kubernaut_incident_response_time"
            legend: "Response Time (seconds)"

      - title: "Alert Noise Reduction"
        type: "stat"
        targets:
          - expr: "kubernaut_alert_noise_reduction_rate"
            legend: "Noise Reduction %"

      - title: "System Availability"
        type: "stat"
        targets:
          - expr: "kubernaut_system_availability"
            legend: "Availability %"
```

#### **AI Decision Audit Trail**
Complete traceability of AI decision-making process during testing.

```go
type AIDecisionAuditTrail struct {
    DecisionID       string                 `json:"decision_id"`
    Timestamp        time.Time              `json:"timestamp"`
    AlertContext     AlertContext           `json:"alert_context"`
    ModelInput       ModelInput             `json:"model_input"`
    ModelOutput      ModelOutput            `json:"model_output"`
    DecisionRationale string                `json:"decision_rationale"`
    ActionsRecommended []RecommendedAction  `json:"actions_recommended"`
    ExecutionResults  []ActionResult        `json:"execution_results"`
    BusinessOutcome   BusinessOutcome       `json:"business_outcome"`
}

// Audit trail validation for business requirements
func (a *AIDecisionAuditTrail) ValidateBusinessRequirement(
    requirementID string,
) (*BusinessRequirementValidation, error) {
    validation := &BusinessRequirementValidation{
        RequirementID: requirementID,
        DecisionID:    a.DecisionID,
        Timestamp:     time.Now(),
    }

    switch requirementID {
    case "BR-AI-001":
        validation.Criteria = []ValidationCriteria{
            {Name: "contextual_awareness", Passed: a.validateContextualAwareness()},
            {Name: "intelligent_analysis", Passed: a.validateIntelligentAnalysis()},
            {Name: "accuracy_threshold", Passed: a.validateAccuracyThreshold(0.90)},
        }

    case "BR-SAFETY-001":
        validation.Criteria = []ValidationCriteria{
            {Name: "fail_safe_operation", Passed: a.validateFailSafeOperation()},
            {Name: "zero_data_loss", Passed: a.validateZeroDataLoss()},
            {Name: "recovery_time", Passed: a.validateRecoveryTime(5 * time.Minute)},
        }
    }

    validation.OverallPassed = a.calculateOverallValidation(validation.Criteria)
    return validation, nil
}
```

---

### **8. Maintenance & Efficiency Tools**

#### **Test Environment Provisioning**
Automated Kind cluster setup/teardown for testing.

```bash
#!/bin/bash
# Test Environment Provisioning Script
# Extends existing docs/development/e2e-testing/deploy-kcli-cluster.sh

./scripts/provision-e2e-environment.sh \
  --cluster-size medium \
  --chaos-enabled true \
  --monitoring-stack full \
  --ai-model oss-gpt:20b \
  --vector-db postgresql \
  --test-data realistic \
  --duration 8h \
  --auto-cleanup true
```

#### **Configuration Management**
Environment-specific test configuration management.

```yaml
# Environment Configuration Templates
environments:
  development:
    cluster:
      nodes: 3
      resources: minimal
    ai_model:
      endpoint: "localhost:8080"
      model: "gpt-oss:20b"
      timeout: "30s"
    database:
      type: "postgresql"
      connection: "localhost:5432"
    chaos:
      enabled: true
      intensity: low

  staging:
    cluster:
      nodes: 6
      resources: standard
    ai_model:
      endpoint: "staging-ai.kubernaut.io:8080"
      model: "gpt-oss:20b"
      timeout: "15s"
    database:
      type: "postgresql"
      connection: "staging-db.kubernaut.io:5432"
    chaos:
      enabled: true
      intensity: medium

  production_testing:
    cluster:
      nodes: 12
      resources: high
    ai_model:
      endpoint: "prod-ai.kubernaut.io:8080"
      model: "gpt-oss:20b"
      timeout: "10s"
    database:
      type: "postgresql"
      connection: "prod-db.kubernaut.io:5432"
    chaos:
      enabled: true
      intensity: high
```

#### **Result Aggregation & Reporting**
Business stakeholder-friendly test reports with actionable insights.

```go
type BusinessStakeholderReport struct {
    ExecutionSummary    ExecutionSummary           `json:"execution_summary"`
    BusinessOutcomes    map[string]BusinessOutcome `json:"business_outcomes"`
    PerformanceMetrics  PerformanceReport          `json:"performance_metrics"`
    RecommendedActions  []RecommendedAction        `json:"recommended_actions"`
    RiskAssessment      RiskAssessment             `json:"risk_assessment"`
}

type ExecutionSummary struct {
    TotalTestCases      int           `json:"total_test_cases"`
    PassedTestCases     int           `json:"passed_test_cases"`
    FailedTestCases     int           `json:"failed_test_cases"`
    ExecutionDuration   time.Duration `json:"execution_duration"`
    BusinessValueScore  float64       `json:"business_value_score"`
    ReadinessAssessment string        `json:"readiness_assessment"`
}

// Generate business stakeholder report
func (r *BusinessOutcomeAggregator) GenerateStakeholderReport(
    results []TestExecutionResult,
) (*BusinessStakeholderReport, error) {
    report := &BusinessStakeholderReport{
        ExecutionSummary:   r.aggregateExecutionSummary(results),
        BusinessOutcomes:   r.aggregateBusinessOutcomes(results),
        PerformanceMetrics: r.aggregatePerformanceMetrics(results),
    }

    // Generate actionable recommendations
    report.RecommendedActions = r.generateRecommendations(report)
    report.RiskAssessment = r.assessRisks(report)

    return report, nil
}
```

---

## 🚀 **Implementation Roadmap**

### **Phase 1: Foundation (Weeks 1-2)**
- **Enhanced Test Factory**: Extend existing `StandardTestSuite` with e2e capabilities
- **Chaos Integration**: LitmusChaos integration with Ginkgo/Gomega framework
- **Business Metrics Engine**: Implement automated BR-XXX requirement validation
- **AI Stress Testing**: Basic oss-gpt:20b load testing framework

### **Phase 2: Core Capabilities (Weeks 3-4)**
- **Chaos Orchestration**: Automated experiment scheduling and validation
- **Real-time Monitoring**: Grafana dashboards and SLA monitoring
- **Test Data Management**: Realistic alert generation and pattern injection
- **Integration Pipeline**: Automated test matrix execution

### **Phase 3: Advanced Features (Weeks 5-6)**
- **Comprehensive Reporting**: Business stakeholder reports and recommendations
- **Maintenance Automation**: Self-healing test infrastructure
- **Performance Optimization**: Test execution efficiency improvements
- **Documentation**: Complete framework documentation and training materials

### **Success Metrics**
- **Development Velocity**: 60% reduction in test implementation time
- **Test Reliability**: 95% test stability and repeatability
- **Business Value**: 100% BR-XXX requirement coverage validation
- **Maintenance Overhead**: 70% reduction in manual testing maintenance

This framework provides a comprehensive foundation for implementing the Top 10 E2E Use Cases while ensuring business outcome validation, development efficiency, and operational excellence.
