Testing and QA Strategy
Overview
Comprehensive testing strategies covering integration testing, data quality testing, user migration testing, and user acceptance testing. These strategies validate Salesforce configurations, integrations, and portal functionality across multiple environments.
Integration Testing
Connectivity Testing
Pattern: Test connectivity between all integration layers
Implementation:
- Test connectivity for Salesforce → MuleSoft → external APIs
- Verify SIS integration jobs in Boomi
- Validate network connectivity and authentication
- Test error handling and retry logic
- Validate API authentication and authorization
Data Transformation Validation
Pattern: Validate data transformation and mapping at each layer
Implementation:
- Validate data transformation in MuleSoft (DataWeave)
- Validate data transformation in Boomi
- Verify field mappings between systems
- Test data type conversions
- Validate null value handling
Error Handling Validation
Pattern: Test error handling and retry logic for transient failures
Implementation:
- Test error handling in Integration Procedures
- Validate retry logic for transient failures
- Test error message display to users
- Validate error logging
- Test error recovery workflows
Performance Testing
Pattern: Performance test high-volume integration scenarios
Implementation:
- Test high-volume batch synchronization
- Validate API rate limit handling
- Test file-based staging for large data sets
- Monitor performance metrics
- Identify performance bottlenecks
Data Quality Testing
Data Quality Tool Behavior
Pattern: Validate data quality tool behavior with realistic test data
Implementation:
- Test real-time matching and deduplication
- Validate error capture and troubleshooting capabilities
- Test custom logic for specific lead types and owners
- Replicate errors, especially around default record types and conversion paths
- Validate lead-to-contact conversion workflows
Matching and Deduplication
Pattern: Test matching and deduplication logic
Implementation:
- Test matching rules with various data scenarios
- Validate deduplication behavior
- Test error handling for conversion failures
- Validate data quality tool configuration
- Test with realistic production-like data
Error Capture and Troubleshooting
Pattern: Validate error capture and troubleshooting capabilities
Implementation:
- Test error field population on Lead records
- Validate error message clarity
- Test error investigation workflows
- Validate SOQL queries for error analysis
- Test error resolution processes
User Migration and Login Handler Testing
User Migration Flows
Pattern: Test user migration flows for all user types
Implementation:
- Test user migration flows for clients
- Test user migration flows for external partner staff
- Test user migration flows for internal staff
- Validate Contact pre-creation from migrations
- Test User creation on first login scenarios
Login Handler Flows
Pattern: Test login handler flows for all identity providers
Implementation:
- Test OIDC login handler flows
- Test SAML login handler flows
- Test organization tenant login handler flows
- Validate identity mapping (GUID to Contact matching)
- Test routing to appropriate landing pages
Identity Mapping Validation
Pattern: Validate identity mapping logic
Implementation:
- Test external identity provider GUID to Contact matching
- Validate email-based fallback matching
- Test edge cases (identity exists but Contact doesn’t, vice versa)
- Validate User creation on first login
- Test duplicate prevention logic
User Acceptance Testing (UAT)
UAT Test Instructions
Pattern: Create step-by-step UAT test instructions for business users
Implementation:
- Step-by-step test instructions for data quality tool behavior
- Test instructions for new advisor-task flows
- Test instructions for employer-engagement processes
- Test instructions for portal functionality for different user types
- Test instructions for case management workflows
- Test instructions for integration-driven data synchronization
Business Process Validation
Pattern: Validate business processes end-to-end
Implementation:
- Test all user-facing functionality in full sandbox
- Validate business processes end-to-end
- Test with realistic business scenarios
- Validate user experience and workflows
- Document UAT results and feedback
UAT Coordination
Pattern: Coordinate UAT with business stakeholders
Implementation:
- Schedule UAT sessions with business users
- Provide clear test instructions
- Support business users during UAT
- Document UAT findings
- Address UAT findings before production deployment
Test Environment Management
Environment Strategy
Environments:
- DEV - Development environment
- QA - Quality assurance environment
- PERF - Performance testing environment
- UAT - User acceptance testing environment
- PROD - Production environment
Test Data Management
Pattern: Use realistic test data that mirrors production scenarios
Implementation:
- Use realistic test data for all test scenarios
- Refresh test data regularly to maintain data quality
- Create test data factories for consistent test scenarios
- Isolate test data between test environments
- Document test data requirements and sources
Environment-Specific Configuration
Pattern: Validate environment-specific configuration
Implementation:
- Validate Named Credentials configuration per environment
- Validate Custom Metadata configuration per environment
- Test environment-specific endpoints
- Validate integration platform configuration per environment
- Document environment-specific differences
Test Class Anti-Patterns and Security
Test Class Security Anti-Pattern
When NOT to do: Including test classes in permission sets or profiles accessible to end users.
Why it’s a problem: Test classes can expose test logic, data, and potentially sensitive information to unauthorized users. This is a security risk and violates best practices.
Real example: Found test classes (CommunitiesLandingControllerTest, CommunitiesLoginControllerTest) in permission sets during code review. Removed them immediately.
Prevention:
- Test classes should never be included in permission sets
- Test classes should never be accessible to end users
- Always review permission sets for test classes during security reviews
- Remove test classes from permission sets if found
Test Class Design Anti-Patterns
Anti-pattern: Using @SeeAllData
- Problem: Test classes that use
@IsTest(SeeAllData=true)depend on org-specific data - Solution: ALL test classes MUST create their own test data (no
@IsTest(SeeAllData=true)) - Why: Tests must be self-contained and reliable, not dependent on org-specific data
Anti-pattern: Single-record tests
- Problem: Testing with single records doesn’t catch bulkification issues
- Solution: Always test with bulk data (200+ records minimum)
- Why: Production code must handle bulk operations correctly
Anti-pattern: Testing only successful scenarios
- Problem: Only testing positive scenarios misses error handling and edge cases
- Solution: Test both positive and negative scenarios, error handling, and edge cases
- Why: Real-world scenarios include failures and edge cases
Anti-pattern: Complex logic in Test.startTest() blocks
- Problem: Complex logic within
Test.startTest()andTest.stopTest()blocks can mask limit issues - Solution: Minimize logic within these blocks, keep it focused on the operation being tested
- Why: These blocks reset governor limits, but complex logic can hide limit problems
Test Coverage Requirements
Code Coverage
Standards:
- Aim for 100% code coverage (minimum 90%)
- Test both positive and negative scenarios
- Test with bulk data (200 records minimum)
- Use
Test.startTest()andTest.stopTest()to reset governor limits
Integration Test Coverage
Coverage:
- All integration endpoints tested
- All data transformation logic validated
- All error scenarios tested
- All retry logic validated
- Performance tested for high-volume scenarios
UAT Coverage
Coverage:
- All user-facing functionality tested
- All business processes validated
- All user types tested
- All portal functionality validated
- All integration-driven workflows tested
Best Practices
Integration Testing
- Test connectivity between all integration layers
- Validate data transformation and mapping at each layer
- Test error handling and retry logic for transient failures
- Validate API authentication and authorization
- Performance test high-volume integration scenarios
- Document integration test results and issues
Data Quality Testing
- Test data quality tool behavior with realistic test data
- Validate matching and deduplication logic
- Test error capture and troubleshooting capabilities
- Replicate production errors in test environments
- Validate custom logic for specific scenarios
- Document data quality test results and fixes
Identity and User Migration Testing
- Test user migration flows for all user types
- Validate login handler flows for all identity providers
- Test identity mapping (GUID to Contact matching)
- Validate user creation on first login scenarios
- Test routing to appropriate landing pages
- Document identity testing results and issues
User Acceptance Testing
- Create step-by-step UAT test instructions for business users
- Test all user-facing functionality in full sandbox
- Validate business processes end-to-end
- Document UAT results and feedback
- Address UAT findings before production deployment
Test Data Management
- Use realistic test data that mirrors production scenarios
- Refresh test data regularly to maintain data quality
- Create test data factories for consistent test scenarios
- Isolate test data between test environments
- Document test data requirements and sources
Test Class Design Best Practices
Design for testability from the start:
- Use dependency injection patterns
- Define interfaces for dependencies (e.g.,
IContactSelector,IExternalApiService) - Inject dependencies through constructors or setter methods
- Enable mocking in test classes
Test integration error scenarios:
- Use callout mocks to test error scenarios (network failures, timeouts, invalid responses)
- Don’t just test successful scenarios
- Test error handling and retry logic
- Validate error messages and recovery workflows
Tradeoffs
Advantages
- Comprehensive validation of functionality
- Early identification of issues
- Confidence in production deployment
- Stakeholder alignment
- Quality assurance
Challenges
- Time-intensive testing process
- Requires coordination across teams
- Test data management complexity
- Environment management overhead
- Documentation requirements
When to Use This Strategy
Use this testing strategy when:
- Complex integrations with external systems
- Multiple user types and identity providers
- High quality and compliance requirements
- Government/compliance projects
- Large-scale implementations
When Not to Use This Strategy
Avoid this strategy when:
- Simple implementations with minimal integration
- Rapid prototyping needs
- Different testing approach preferred
- Minimal quality requirements
- Time constraints prevent comprehensive testing
Q&A
Q: What types of testing should I include in a comprehensive testing strategy?
A: Include: (1) Integration testing (connectivity, data transformation, error handling), (2) Data quality testing (data validation, completeness, accuracy), (3) User migration testing (login handlers, identity provider integration), (4) User Acceptance Testing (UAT) (business user validation), (5) Unit testing (Apex test classes), (6) Performance testing (load, stress testing), (7) Security testing (access controls, vulnerabilities).
Q: How do I test integrations with external systems?
A: Test integrations by: (1) Testing connectivity (Salesforce → MuleSoft → external APIs, verify network, authentication), (2) Validating data transformation (DataWeave in MuleSoft, field mappings, data types), (3) Testing error handling (retry logic, error messages, error logging), (4) Testing end-to-end flows (complete integration scenarios), (5) Testing with realistic data (production-like test data), (6) Monitoring integration health (track success/failure rates).
Q: What is data quality testing and why is it important?
A: Data quality testing validates data completeness, accuracy, consistency, and validity. It’s important because: (1) Poor data quality causes business issues, (2) Integration failures often due to data quality, (3) User experience depends on data quality, (4) Compliance requires data quality. Test: data validation rules, required fields, data formats, data relationships, duplicate detection.
Q: How do I test user migration and login handlers?
A: Test user migration by: (1) Testing login handlers (OIDC, SAML, organization tenant), (2) Testing user creation (first-time login, pre-created contacts), (3) Testing duplicate prevention (matching on GUID, email), (4) Testing identity mapping (external IDs to Salesforce records), (5) Testing error scenarios (failed authentication, missing contacts), (6) Testing with real identity providers (test environments).
Q: What should be included in User Acceptance Testing (UAT)?
A: Include in UAT: (1) Business process validation (end-to-end business processes), (2) User-facing functionality (all user-facing features), (3) Role-based testing (test with different user roles), (4) Real-world scenarios (actual business scenarios), (5) Feedback collection (structured feedback, issue tracking), (6) Documentation (UAT results, findings), (7) Issue resolution (address findings before production).
Q: How do I manage test environments?
A: Manage test environments by: (1) Providing appropriate environments (sandboxes for different test types), (2) Refreshing test data regularly (maintain data quality), (3) Isolating test data between environments, (4) Creating test data factories (consistent test scenarios), (5) Documenting test data requirements, (6) Coordinating environment access (schedule testing windows), (7) Maintaining environment parity (keep environments similar to production).
Q: What are test class anti-patterns to avoid?
A: Avoid: (1) Using @SeeAllData (makes tests dependent on org data), (2) Not bulkifying tests (test with single records only), (3) Missing assertions (tests that don’t verify results), (4) Hard-coded IDs (tests that break when IDs change), (5) Testing implementation details (test behavior, not internals), (6) Not testing error scenarios (only test happy path), (7) Poor test data setup (inconsistent, incomplete test data).
Q: What test coverage requirements should I enforce?
A: Enforce: (1) Minimum 75% coverage for deployment (Salesforce requirement), (2) Target 90%+ coverage for quality (best practice), (3) 100% coverage for critical code (high-risk code), (4) Meaningful coverage (not just hitting lines, but testing behavior), (5) Coverage for all code paths (positive and negative scenarios), (6) Integration test coverage (test integrations, not just unit tests).
Q: How do I test security in my testing strategy?
A: Test security by: (1) Testing access controls (profiles, permission sets, FLS), (2) Testing sharing rules (data visibility, record access), (3) Testing authentication (login handlers, SSO), (4) Testing authorization (who can access what), (5) Security scanning (vulnerability scanning, code review), (6) Penetration testing (manual security testing), (7) Compliance testing (GDPR, HIPAA requirements).
Q: What are best practices for testing strategy?
A: Best practices include: (1) Test early and often (include testing in development cycle), (2) Test with realistic data (production-like test data), (3) Automate testing (CI/CD integration), (4) Test all scenarios (positive, negative, edge cases), (5) Coordinate testing (schedule testing windows with stakeholders), (6) Document test results (comprehensive documentation), (7) Iterate based on findings (improve testing based on results).
Related Patterns
- Apex Testing Patterns - Apex test class patterns
- Test Data Factories - Test data factory patterns
- Automated Testing Patterns - Automated testing approaches
- Delivery Framework - Project delivery methodology