moreminimore-marketing/backend/api/content_planning/tests

Content Planning Module - Testing Foundation

This directory contains comprehensive testing infrastructure for the content planning module refactoring project.

📋 Overview

The testing foundation ensures that all functionality is preserved during the refactoring process by:

1. Establishing Baseline: Comprehensive functionality tests before refactoring
2. Continuous Validation: Testing at each refactoring step
3. Before/After Comparison: Automated response comparison
4. Performance Monitoring: Tracking response times and performance metrics

🧪 Test Scripts

1. functionality_test.py

Purpose: Comprehensive functionality test suite that tests all existing endpoints and functionality.

Features:

• Tests all strategy endpoints (CRUD operations)
• Tests all calendar event endpoints
• Tests gap analysis functionality
• Tests AI analytics endpoints
• Tests calendar generation
• Tests content optimization
• Tests error scenarios and validation
• Tests performance metrics
• Tests response format consistency

Usage:

cd backend/content_planning/tests
python functionality_test.py

2. before_after_test.py

Purpose: Automated comparison of API responses before and after refactoring.

Features:

• Loads baseline data from functionality test results
• Captures responses from refactored API
• Compares response structure and content
• Compares performance metrics
• Generates detailed comparison reports

Usage:

cd backend/content_planning/tests
python before_after_test.py

3. test_data.py

Purpose: Centralized test data and fixtures for consistent testing.

Features:

• Sample strategy data for different industries
• Sample calendar event data
• Sample gap analysis data
• Sample AI analytics data
• Sample error scenarios
• Performance baseline data
• Validation functions

Usage:

from test_data import TestData, create_test_strategy

# Get sample strategy data
strategy_data = TestData.get_strategy_data("technology")

# Create test strategy with custom parameters
custom_strategy = create_test_strategy("healthcare", user_id=2)

4. run_tests.py

Purpose: Simple test runner to execute all tests and establish baseline.

Features:

• Runs baseline functionality test
• Runs before/after comparison test
• Provides summary reports
• Handles test execution flow

Usage:

cd backend/content_planning/tests
python run_tests.py

🚀 Quick Start

Step 1: Establish Baseline

cd backend/content_planning/tests
python run_tests.py

This will:

1. Run comprehensive functionality tests
2. Save baseline results to functionality_test_results.json
3. Print summary of test results

Step 2: Run During Refactoring

After each refactoring step, run:

python run_tests.py

This will:

1. Load existing baseline data
2. Test refactored functionality
3. Compare responses with baseline
4. Report any differences

Step 3: Validate Final Refactoring

After completing the refactoring:

python run_tests.py

This will confirm that all functionality is preserved.

📊 Test Coverage

Endpoint Coverage

• ✅ Health Endpoints: All health check endpoints
• ✅ Strategy Endpoints: CRUD operations, analytics, optimization
• ✅ Calendar Endpoints: Event management, scheduling, conflicts
• ✅ Gap Analysis: Analysis execution, competitor analysis, keyword research
• ✅ AI Analytics: Performance prediction, strategic intelligence
• ✅ Calendar Generation: AI-powered calendar creation
• ✅ Content Optimization: Platform-specific optimization
• ✅ Performance Prediction: Content performance forecasting
• ✅ Content Repurposing: Cross-platform content adaptation
• ✅ Trending Topics: Industry-specific trending topics
• ✅ Comprehensive User Data: All user data aggregation

Test Scenarios

• ✅ Happy Path: Normal successful operations
• ✅ Error Handling: Invalid inputs, missing data, server errors
• ✅ Data Validation: Input validation and sanitization
• ✅ Response Format: Consistent API response structure
• ✅ Performance: Response times and throughput
• ✅ Edge Cases: Boundary conditions and unusual scenarios

📈 Performance Monitoring

Baseline Metrics

• Response Time Threshold: 0.5 seconds
• Status Code: 200 for successful operations
• Error Rate: < 1%

Performance Tracking

• Response times for each endpoint
• Status code consistency
• Error rate monitoring
• Memory usage tracking

🔧 Configuration

Test Environment

• Base URL: http://localhost:8000 (configurable)
• Test Data: Centralized in test_data.py
• Results: Saved as JSON files

Customization

You can customize test parameters by modifying:

• base_url in test classes
• Test data in test_data.py
• Performance thresholds
• Error scenarios

📋 Test Results

Output Files

• functionality_test_results.json: Baseline test results
• before_after_comparison_results.json: Comparison results
• Console output: Real-time test progress and summaries

Result Format

{
  "test_name": {
    "status": "passed|failed",
    "status_code": 200,
    "response_time": 0.12,
    "response_data": {...},
    "error": "error message if failed"
  }
}

🎯 Success Criteria

Functionality Preservation

• ✅ 100% Feature Compatibility: All existing features work identically
• ✅ Response Consistency: Identical API responses before and after
• ✅ Error Handling: Consistent error scenarios and messages
• ✅ Performance: Maintained or improved performance metrics

Quality Assurance

• ✅ Automated Testing: Comprehensive test suite
• ✅ Continuous Validation: Testing at each refactoring step
• ✅ Risk Mitigation: Prevents regressions and functionality loss
• ✅ Confidence Building: Ensures no features are lost during refactoring

🚨 Troubleshooting

Common Issues

1. Connection Errors

   • Ensure the backend server is running on http://localhost:8000
   • Check network connectivity
   • Verify API endpoints are accessible

2. Test Failures

   • Review error messages in test results
   • Check if baseline data exists
   • Verify test data is valid

3. Performance Issues

   • Monitor server performance
   • Check database connectivity
   • Review AI service availability

Debug Mode

Enable debug logging by setting:

import logging
logging.basicConfig(level=logging.DEBUG)

📚 Next Steps

After establishing the testing foundation:

1. Day 1: Extract utilities and test each extraction
2. Day 2: Extract services and validate functionality
3. Day 3: Extract routes and verify endpoints
4. Day 4: Comprehensive testing and validation

Each day should include running the test suite to ensure functionality preservation.

🤝 Contributing

When adding new tests:

1. Add test data to test_data.py
2. Add test methods to functionality_test.py
3. Update comparison logic in before_after_test.py
4. Document new test scenarios

📞 Support

For issues with the testing foundation:

1. Check the troubleshooting section
2. Review test logs and error messages
3. Verify test data and configuration
4. Ensure backend services are running correctly