Migration Test Documentation: Complete Guide for System Transitions

Introduction to Migration Test Documentation

Migration testing is one of the most critical and risk-prone activities in software engineering. Whether migrating data between databases, transitioning to new platforms, upgrading systems, or moving to the cloud, comprehensive test documentation ensures data integrity, system functionality, and business continuity throughout the transition.

This guide provides detailed frameworks, templates, and real-world strategies for documenting migration tests that minimize risks and ensure successful system transitions.

Types of Migrations and Testing Approaches

Common Migration Scenarios

Data Migrations:

• Database platform changes (Oracle → PostgreSQL)
• Data center migrations
• Legacy system retirement
• Data consolidation and deduplication

Application Migrations:

• Platform modernization (Monolith → Microservices)
• Cloud migrations (On-premise → AWS/Azure/GCP)
• Framework upgrades (Angular 8 → Angular 17)
• Version upgrades (Java 11 → Java 21)

Infrastructure Migrations:

• Server migrations
• Network infrastructure changes
• Storage system transitions
• Virtualization and containerization

Migration Risk Assessment

Migration Test Strategy Document

Strategy Template

# MIGRATION TEST STRATEGY

## Executive Summary
**Migration Project**: Legacy CRM to Salesforce Migration
**Migration Date**: 2025-12-15
**Test Manager**: Alex Rodriguez
**Last Updated**: 2025-10-08

## Migration Overview
### Source System
- **Platform**: Custom .NET CRM Application
- **Database**: SQL Server 2016
- **Records**: 2.5M customer records, 15M transactions
- **Interfaces**: 12 integrated systems
- **Uptime**: 24/7 operations

### Target System
- **Platform**: Salesforce Sales Cloud
- **Edition**: Enterprise Edition
- **Customizations**: 45 custom objects, 120 custom fields
- **Integrations**: REST APIs, MuleSoft middleware
- **Expected Performance**: < 2s page load, 10K concurrent users

## Migration Approach
**Strategy**: Phased migration with parallel run

### Phase 1: Pilot Migration (Week 1-2)
- Migrate 10% of customers (250K records)
- Single business unit (West Coast)
- Full functionality testing
- User acceptance testing

### Phase 2: Incremental Migration (Week 3-6)
- Migrate remaining 90% in batches
- 500K records per batch
- Automated validation after each batch
- Continuous monitoring

### Phase 3: Cutover (Week 7)
- Final delta migration
- Switch DNS/routing
- Decommission legacy system
- 48-hour monitoring period

## Test Objectives
1. Verify 100% data migration accuracy
2. Validate all business workflows
3. Confirm integration functionality
4. Ensure performance meets SLAs
5. Validate security and compliance
6. Verify reporting and analytics

## Test Scope

### In Scope
- Data migration validation (all tables)
- Functional testing (critical user journeys)
- Integration testing (all 12 interfaces)
- Performance testing (load and stress)
- Security testing (authentication, authorization)
- UAT (50 business users)

### Out of Scope
- Legacy system bug fixes
- New feature development
- Historical data older than 7 years
- Retired integrations (3 deprecated systems)

## Test Environment Strategy

| Environment | Purpose | Data Volume | Availability |
|------------|---------|-------------|--------------|
| DEV | Development testing | 1% (25K records) | Weekdays 8-6 |
| TEST | Integration testing | 10% (250K records) | 24/7 |
| STAGE | UAT & rehearsal | 20% (500K records) | 24/7 |
| PROD | Live migration | 100% (2.5M records) | Migration window |

## Test Deliverables
- [ ] Migration test plan
- [ ] Test cases and scripts
- [ ] Data validation reports
- [ ] Performance test results
- [ ] UAT sign-off documents
- [ ] Migration runbook
- [ ] Rollback procedure
- [ ] Post-migration validation report

Data Migration Testing

Data Mapping Document

## Data Mapping Specification

### Customer Data Mapping

| Source Field | Source Type | Target Field | Target Type | Transformation | Validation Rule |
|-------------|-------------|--------------|-------------|----------------|-----------------|
| CustomerID | INT | Account.CustomerNumber__c | Text(20) | CAST to VARCHAR | NOT NULL, UNIQUE |
| FirstName | VARCHAR(50) | Account.FirstName | Text(40) | TRIM, UPPERCASE first char | NOT NULL |
| LastName | VARCHAR(50) | Account.LastName | Text(80) | TRIM, UPPERCASE first char | NOT NULL |
| Email | VARCHAR(100) | Account.Email__c | Email | LOWERCASE, validate format | Valid email format |
| PhoneNumber | VARCHAR(20) | Account.Phone | Phone | Format: (XXX) XXX-XXXX | Valid phone format |
| CreateDate | DATETIME | Account.CreatedDate | DateTime | Convert to UTC | NOT NULL |
| Status | TINYINT | Account.Status__c | Picklist | 0→Inactive, 1→Active | Valid picklist value |
| CreditLimit | DECIMAL(10,2) | Account.CreditLimit__c | Currency | Direct mapping | >= 0 |

### Data Transformation Rules

**Rule 1: Name Standardization**
```sql
-- Source
FirstName = ' john ', LastName = ' DOE '

-- Transformation
FirstName = TRIM(INITCAP(FirstName)) -- 'John'
LastName = TRIM(UPPER(LastName)) -- 'DOE'

-- Target
FirstName = 'John', LastName = 'DOE'

Rule 2: Status Code Mapping

status_mapping = {
    0: 'Inactive',
    1: 'Active',
    2: 'Suspended',
    3: 'Pending',
    99: 'Deleted'
}

# Apply transformation
target_status = status_mapping.get(source_status, 'Unknown')

Rule 3: Date Timezone Conversion

from datetime import datetime
import pytz

def convert_to_utc(local_datetime, source_timezone='US/Pacific'):
    """Convert local datetime to UTC for Salesforce"""
    local_tz = pytz.timezone(source_timezone)
    local_dt = local_tz.localize(local_datetime)
    utc_dt = local_dt.astimezone(pytz.UTC)
    return utc_dt

### Data Validation Framework

```python
# Migration Data Validation Framework

import pandas as pd
from typing import Dict, List, Tuple

class MigrationValidator:
    """Comprehensive data validation for migrations"""

    def __init__(self, source_conn, target_conn):
        self.source_conn = source_conn
        self.target_conn = target_conn
        self.validation_results = []

    def validate_record_counts(self, table_mapping: Dict[str, str]) -> Dict:
        """
        Validate record counts between source and target

        Args:
            table_mapping: Dict of {source_table: target_table}

        Returns:
            Dict with validation results
        """
        results = {}

        for source_table, target_table in table_mapping.items():
            source_count = self._get_count(self.source_conn, source_table)
            target_count = self._get_count(self.target_conn, target_table)

            match = source_count == target_count
            results[source_table] = {
                'source_count': source_count,
                'target_count': target_count,
                'match': match,
                'difference': source_count - target_count
            }

        return results

    def validate_data_integrity(self, validation_queries: List[Tuple]) -> pd.DataFrame:
        """
        Execute data integrity validation queries

        Args:
            validation_queries: List of (description, source_query, target_query)

        Returns:
            DataFrame with validation results
        """
        results = []

        for description, source_query, target_query in validation_queries:
            source_result = pd.read_sql(source_query, self.source_conn)
            target_result = pd.read_sql(target_query, self.target_conn)

            # Compare results
            match = source_result.equals(target_result)

            results.append({
                'validation': description,
                'source_value': source_result.iloc[0, 0] if not source_result.empty else None,
                'target_value': target_result.iloc[0, 0] if not target_result.empty else None,
                'status': 'PASS' if match else 'FAIL'
            })

        return pd.DataFrame(results)

    def validate_referential_integrity(self, foreign_keys: Dict) -> Dict:
        """
        Validate foreign key relationships are maintained

        Args:
            foreign_keys: Dict of relationship definitions

        Returns:
            Dict with validation results
        """
        results = {}

        for relationship_name, relationship_def in foreign_keys.items():
            # Check orphaned records in target
            query = f"""
                SELECT COUNT(*) as orphaned_count
                FROM {relationship_def['target_child_table']}
                WHERE {relationship_def['foreign_key']} NOT IN (
                    SELECT {relationship_def['primary_key']}
                    FROM {relationship_def['target_parent_table']}
                )
            """

            orphaned = pd.read_sql(query, self.target_conn).iloc[0, 0]

            results[relationship_name] = {
                'orphaned_records': orphaned,
                'status': 'PASS' if orphaned == 0 else 'FAIL'
            }

        return results

    def validate_data_quality(self, quality_rules: List) -> pd.DataFrame:
        """
        Validate data quality rules

        Args:
            quality_rules: List of quality rule definitions

        Returns:
            DataFrame with validation results
        """
        results = []

        for rule in quality_rules:
            query = f"""
                SELECT COUNT(*) as violation_count
                FROM {rule['table']}
                WHERE NOT ({rule['condition']})
            """

            violations = pd.read_sql(query, self.target_conn).iloc[0, 0]

            results.append({
                'rule': rule['name'],
                'table': rule['table'],
                'violations': violations,
                'status': 'PASS' if violations == 0 else 'FAIL'
            })

        return pd.DataFrame(results)

    def generate_validation_report(self) -> str:
        """Generate comprehensive validation report"""
        report = "# MIGRATION VALIDATION REPORT\n\n"
        report += f"Generated: {datetime.now().isoformat()}\n\n"

        # Add all validation results
        for validation_type, results in self.validation_results:
            report += f"## {validation_type}\n\n"
            report += results.to_markdown() if isinstance(results, pd.DataFrame) else str(results)
            report += "\n\n"

        return report


# Example Usage
validation_queries = [
    (
        "Total Customer Count",
        "SELECT COUNT(*) FROM Customers WHERE Status = 1",
        "SELECT COUNT(*) FROM Account WHERE Status__c = 'Active'"
    ),
    (
        "Total Revenue",
        "SELECT SUM(Amount) FROM Orders WHERE OrderDate >= '2023-01-01'",
        "SELECT SUM(Amount__c) FROM Order__c WHERE OrderDate__c >= 2023-01-01"
    ),
    (
        "Unique Email Addresses",
        "SELECT COUNT(DISTINCT Email) FROM Customers",
        "SELECT COUNT(DISTINCT Email__c) FROM Account"
    )
]

# Run validation
validator = MigrationValidator(source_conn, target_conn)
integrity_results = validator.validate_data_integrity(validation_queries)
print(integrity_results)

Migration Test Cases

Critical Test Scenarios

## Migration Test Cases

### TC-MIG-001: Record Count Validation
**Priority**: P0 - Critical
**Category**: Data Validation

**Pre-conditions**:
- Migration completed
- Both source and target systems accessible

**Test Steps**:
1. Query record count from source system: `SELECT COUNT(*) FROM Customers`
2. Query record count from target system: `SELECT COUNT(*) FROM Account`
3. Compare counts
4. Verify difference = 0

**Expected Result**:
- Source count = 2,500,000
- Target count = 2,500,000
- Difference = 0

**Actual Result**: [To be filled during execution]

**Status**: [PASS/FAIL]

---

### TC-MIG-002: Data Transformation Validation
**Priority**: P0 - Critical
**Category**: Data Validation

**Pre-conditions**:
- Sample data set identified (100 records)
- Transformation rules documented

**Test Steps**:
1. Select 100 random records from source
2. Apply transformation rules manually
3. Compare with migrated data in target
4. Verify all transformations correct

**Expected Result**: 100/100 records match expected transformations

**Test Data**:

CustomerID: 12345 Source Name: " john doe " Expected Target Name: “John DOE” Source Status: 1 Expected Target Status: “Active”

---

### TC-MIG-003: Referential Integrity Validation
**Priority**: P0 - Critical
**Category**: Data Validation

**Test Steps**:
1. Validate all Customer-Order relationships preserved
2. Check for orphaned Orders (Orders without Customers)
3. Validate Order-OrderItem relationships
4. Verify no referential integrity violations

**Validation Queries**:
```sql
-- Check for orphaned orders
SELECT COUNT(*) as orphaned_orders
FROM Order__c o
WHERE o.AccountId NOT IN (SELECT Id FROM Account);

-- Expected: 0

Expected Result: Zero orphaned records

TC-MIG-004: Business Workflow Validation

Priority: P1 - High Category: Functional Testing

Test Steps:

1. Create new customer in target system
2. Create order for customer
3. Process payment
4. Generate invoice
5. Verify workflow completion

Expected Result: All steps complete successfully, matching legacy behavior

TC-MIG-005: Integration Testing

Priority: P1 - High Category: Integration Testing

Systems to Validate:

• Email notification system
• Payment gateway
• Shipping system
• Reporting/BI system

Test Steps (for each integration):

1. Trigger integration event
2. Verify data transmission
3. Validate data format
4. Confirm successful processing
5. Check error handling

Expected Result: All 12 integrations functioning correctly

TC-MIG-006: Performance Validation

Priority: P1 - High Category: Performance Testing

Test Scenarios:

1. Page load time: < 2 seconds (95th percentile)
2. Search response: < 1 second
3. Report generation: < 5 seconds
4. Concurrent users: 10,000 users without degradation

Test Method: Load testing with JMeter/Gatling

Expected Result: All performance SLAs met or exceeded

## Migration Rollback Plan

### Rollback Strategy

```markdown
# MIGRATION ROLLBACK PLAN

## Rollback Decision Criteria

### Severity 1: Immediate Rollback
- Data loss detected (> 0.1%)
- Critical business process failure
- Security breach or data exposure
- Complete system unavailability
- **Decision Time**: < 30 minutes
- **Decision Maker**: CTO + Migration Lead

### Severity 2: Planned Rollback
- Performance degradation > 50%
- Major functionality broken (affecting > 20% users)
- Multiple integration failures
- Data quality issues > 5%
- **Decision Time**: < 2 hours
- **Decision Maker**: Project Steering Committee

## Rollback Procedures

### Step 1: Communication (0-15 minutes)
```bash
# Execute communication plan
./scripts/send_rollback_notification.sh

# Notify stakeholders
- Engineering team
- Customer support
- Business users
- Executive team

Step 2: Freeze State (15-30 minutes)

# Stop all data synchronization
./scripts/stop_sync_services.sh

# Capture current state
./scripts/capture_system_state.sh

# Enable maintenance mode
./scripts/enable_maintenance_mode.sh

Step 3: Restore Database (30-90 minutes)

# Restore source database from backup
pg_restore --host=source-db.internal \
           --dbname=crm_production \
           --clean --create \
           /backups/pre_migration_backup.dump

# Verify restore
./scripts/verify_database_restore.sh

Step 4: Redirect Traffic (90-105 minutes)

# Update DNS/Load balancer
./scripts/redirect_to_legacy.sh

# Verify routing
curl -I https://crm.company.com
# Expected: X-Server: legacy-crm-01

Step 5: Validation (105-135 minutes)

# Execute smoke tests
./scripts/run_smoke_tests.sh

# Verify critical workflows
./scripts/verify_critical_workflows.sh

# Check integrations
./scripts/test_integrations.sh

Step 6: Resume Operations (135-150 minutes)

# Disable maintenance mode
./scripts/disable_maintenance_mode.sh

# Monitor system health
./scripts/monitor_system_health.sh --duration=4h

# Send completion notification
./scripts/send_rollback_complete.sh

Rollback Validation Checklist

• Database restored to pre-migration state
• All services running normally
• Critical workflows functional
• Integrations operational
• No data loss confirmed
• Performance metrics normal
• Users able to access system
• Support team briefed
• Incident report initiated

## Post-Migration Validation

### Validation Checklist

```markdown
# POST-MIGRATION VALIDATION CHECKLIST

## Data Validation (Day 1)
- [ ] Record counts match (100% accuracy)
- [ ] Data transformations correct (sample validation)
- [ ] Referential integrity maintained (zero orphans)
- [ ] Data quality rules pass (zero violations)
- [ ] Historical data accessible
- [ ] No duplicate records
- [ ] All mandatory fields populated

## Functional Validation (Day 1-3)
- [ ] Critical user journeys functional (100%)
- [ ] All business workflows operational
- [ ] User authentication working
- [ ] Authorization/permissions correct
- [ ] Search functionality operational
- [ ] Reporting accurate
- [ ] Document/file attachments accessible

## Integration Validation (Day 1-7)
- [ ] All 12 integrations functional
- [ ] Data synchronization working
- [ ] API endpoints responding
- [ ] Error handling appropriate
- [ ] Performance acceptable
- [ ] Monitoring alerts configured

## Performance Validation (Day 1-14)
- [ ] Page load times meet SLA (< 2s)
- [ ] Search response times acceptable (< 1s)
- [ ] Report generation within limits (< 5s)
- [ ] Concurrent user capacity confirmed (10K users)
- [ ] Database query performance optimized
- [ ] No memory leaks detected

## Security Validation (Day 1-7)
- [ ] Authentication functioning
- [ ] Authorization rules enforced
- [ ] Data encryption verified
- [ ] Audit logs operational
- [ ] Compliance requirements met
- [ ] Security scan completed (zero critical issues)

## User Acceptance (Day 7-14)
- [ ] UAT sign-off received (all business units)
- [ ] Training completed
- [ ] User documentation updated
- [ ] Support team trained
- [ ] Feedback collected and addressed

## Operational Validation (Day 1-30)
- [ ] Backups operational
- [ ] Monitoring configured
- [ ] Alerting functional
- [ ] Incident response tested
- [ ] Performance baselines established
- [ ] Capacity planning updated

## Decommissioning (Day 30-60)
- [ ] Legacy system access revoked
- [ ] Final data archive completed
- [ ] Hardware decommissioned
- [ ] Licenses terminated
- [ ] Documentation archived
- [ ] Project retrospective completed

Conclusion

Successful migration testing requires meticulous planning, comprehensive documentation, and rigorous validation at every stage. By following the frameworks, templates, and best practices outlined in this guide, organizations can minimize migration risks, ensure data integrity, and achieve smooth system transitions.

Remember that migration testing is not just about moving data—it’s about ensuring business continuity, maintaining data quality, validating functionality, and setting the foundation for future success. Invest adequate time in preparation, validation, and contingency planning to maximize the chances of migration success.