Integration tests

[UJ2202]

User Story

As a developer and DevOps engineer, I want comprehensive integration tests so that I can confidently deploy CMBCluster knowing that all components work together correctly across different cloud providers and configurations.

Description

Implement a robust integration testing suite that validates end-to-end functionality of CMBCluster across multiple cloud providers, deployment configurations, and user workflows.

Current Testing Analysis

Based on existing testing infrastructure:

• Integration Tests: Basic tests in test_integration.py
• Unit Tests: Backend tests in backend/tests/
• Testing Documentation: Guidelines in TESTING_README.md
• Manual Testing: User test cases documented in UI_TEST_CASES.md

Integration Testing Requirements

1. Deployment Testing

   • GCP deployment validation
   • AWS deployment validation
   • Local development environment testing
   • Database migration testing (SQLite → PostgreSQL)

2. Multi-Cloud Functionality Testing

   • Cross-cloud feature parity validation
   • Storage integration testing (GCS vs S3)
   • Authentication flow testing (Workload Identity vs IRSA)
   • Performance comparison between cloud providers

3. User Workflow Testing

   • Complete user journey from login to environment creation
   • Environment lifecycle testing (create, start, stop, delete)
   • Storage management workflows
   • File upload and environment variable management

4. System Integration Testing

   • Backend API integration
   • Frontend-backend communication
   • Kubernetes pod management
   • Database operations and consistency

Test Framework Architecture

tests/
├── integration/
│   ├── test_deployment_gcp.py      # GCP deployment tests
│   ├── test_deployment_aws.py      # AWS deployment tests
│   ├── test_user_workflows.py      # End-to-end user tests
│   ├── test_multi_cloud.py         # Cross-cloud compatibility
│   ├── test_database_migration.py  # Database migration tests
│   └── test_performance.py         # Performance and load tests
├── fixtures/
│   ├── test_data.py                # Test data and fixtures
│   ├── cloud_configs.py            # Cloud provider configurations
│   └── user_scenarios.py           # User workflow scenarios
└── utils/
    ├── test_helpers.py              # Test utility functions
    ├── cloud_setup.py               # Cloud environment setup
    └── assertions.py                # Custom test assertions

Acceptance Criteria

• Integration tests cover all major user workflows
• Tests validate functionality on both GCP and AWS
• Database migration testing ensures data integrity
• Performance tests establish baseline metrics
• Tests can run in CI/CD pipeline automatically
• Test results provide clear pass/fail status
• Failed tests provide actionable error messages
• Test suite runs in reasonable time (< 30 minutes)
• Tests are maintainable and well-documented

Key Test Scenarios

Deployment Testing:

• Fresh GCP cluster deployment and validation
• Fresh AWS cluster deployment and validation
• Upgrade deployment testing
• Rollback scenario testing

User Workflow Testing:

• User registration and authentication flow
• Environment creation with different configurations
• Storage bucket creation and management
• File upload and environment variable configuration
• Environment scaling and resource management

Cross-Cloud Testing:

• Feature parity validation between GCP and AWS
• Performance comparison between cloud providers
• Data migration between cloud providers
• Configuration portability testing

Technical Implementation

• Use pytest framework for test organization and execution
• Implement test fixtures for cloud provider setup
• Create helper functions for common operations
• Add test data management and cleanup procedures
• Integrate with CI/CD pipeline for automated testing

Test Data Management

• Create representative test datasets
• Implement test data cleanup procedures
• Use isolated test environments
• Ensure test data privacy and security

Performance Testing

• Establish performance baselines for each cloud provider
• Test system behavior under load
• Validate resource scaling functionality
• Monitor and report performance regressions

Related to

Epic #28 - Documentation & Testing

Definition of Done

• Integration test suite covers all critical functionality
• Tests pass consistently on both GCP and AWS
• Test execution is automated in CI/CD pipeline
• Test documentation enables easy maintenance
• Performance baselines are established and monitored