Testing and Quality Assurance
1. Test Environment Requirements
2. Test Categories
2.1. Deployment Testing
2.1.1. Deployment
Docker Deployment:
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Container image validation
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Docker Compose configuration testing
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Environment variable injection
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Volume mounting and persistence
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Container networking
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Health check validation
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Log aggregation
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Resource limit enforcement
Common Validation Points:
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Application startup sequence completion
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Liquibase migration success
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Hazelcast cluster join verification
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Protocol listener initialization (TR-069, MQTT, USP)
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Web service endpoint availability
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Log file creation and rotation
2.2. Functional Testing
2.2.1. Core Device Lifecycle
Device Registration Flow:
Test Cases:
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Initial device registration
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First-time device connection
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Serial number validation
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Device model detection
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Initial parameter retrieval
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Device provisioning
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Configuration parameter setting
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Firmware version verification
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Service activation
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Parameter validation
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Periodic inform handling
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Scheduled inform processing
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Connection request response
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State synchronization
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Device re-registration
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Factory reset scenario
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Configuration restoration
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Historical data preservation
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2.2.2. Windstream Complex Flows
Validation Requirements:
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End-to-end Windstream-specific workflows
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Multi-step provisioning sequences
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Custom parameter mappings
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Integration points validation
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Error handling and recovery
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Performance under Windstream load patterns
Test Scenarios:
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New subscriber activation
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Service modification workflows
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Troubleshooting procedures
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Bulk operations specific to Windstream requirements
2.2.3. Update Group Testing
Basic Update Group Operations:
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Group creation and configuration
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Group definition
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Device membership assignment
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Update policy configuration
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Scheduling parameters
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Firmware update execution
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Update initiation
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Progress tracking
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Success/failure handling
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Rollback procedures
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Configuration update execution
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Parameter updates to device groups
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Validation of applied changes
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Conflict resolution
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Multi-instance coordination
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Update distribution across ACS instances
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Cache synchronization via Hazelcast
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Database locking and consistency
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2.2.4. QoE Testing
Basic QoE Data Flow:
Test Cases:
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QoE data ingestion
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Data reception from devices
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Pass-through to ClickHouse
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Ingestion rate validation
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Data integrity verification
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Historical data retrieval
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Query performance
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Data aggregation accuracy
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Time-range queries
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Grafana dashboard rendering
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High-volume scenarios
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Multiple devices reporting simultaneously
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Sustained high data rates
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Storage capacity monitoring
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2.3. Load Testing
2.3.1. Test Methodology
Comparative Analysis Goals:
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Version Comparison: Quantify performance improvements between old and new ACS versions
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Deployment Method Comparison: Identify performance differences between Docker and manual deployments
Test Approach:
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Execute identical test scenarios across all configurations:
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Old version (baseline)
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New version - Manual deployment
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New version - Docker deployment
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Use the same device simulation tools and scripts
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Use the same database and infrastructure configuration
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Monitor in parallel with Grafana
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Control the test environment (same hardware/network)
2.3.2. Device Load Testing
Test Scenarios:
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Concurrent device connections
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Gradual ramp-up: 100, 500, 1000, 5000, 10000+ devices
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Connection rate: devices per second
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Sustained connection maintenance
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Memory and CPU utilization tracking
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Inform message processing
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Peak inform rate handling
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Response time measurement (target: < 5 seconds)
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Database query patterns
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Cache hit ratios
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Concurrent operations
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Mixed workload: inform + configuration + firmware updates
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Multi-protocol simultaneous operations
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Instance failover during load
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2.3.3. Update Group Load Testing
Large-Scale Update Operations:
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Mass firmware updates
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Update groups: 1000, 5000, 10000+ devices
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Parallel download handling
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Database write performance
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Network bandwidth utilization
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Hazelcast synchronization overhead
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Configuration mass updates
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Bulk parameter changes
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Staged rollout performance
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Success rate tracking
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Metrics to Capture:
| Metric | Measurement |
|---|---|
Update initiation time |
Time from trigger to first device notification |
Completion time |
Time to complete entire group update |
Success rate |
Percentage of successful updates |
Database transactions/sec |
Peak and average TPS |
Memory consumption |
Per-instance memory usage |
CPU utilization |
Per-instance CPU usage |
Network throughput |
Bandwidth utilization |
Cache efficiency |
Hazelcast hit/miss ratios |
2.4. Automation Testing
2.4.1. API Testing Coverage
SOAP Web Services (ACSWS):
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All WSDL operations tested
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Request/response validation
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Error handling scenarios
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Authentication mechanisms
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Concurrent request handling
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Long-running operations
REST API:
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All endpoint operations (GET, POST, PUT, DELETE)
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Request validation (schema, parameters)
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Response validation (status codes, payloads)
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Authentication and authorization
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Rate limiting behavior
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Pagination handling
Subscription API:
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Subscription lifecycle operations
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Event delivery validation
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Filter and query operations
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Webhook functionality
2.4.2. Automation Framework
Tools and Technologies:
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REST API: Postman/Newman, RestAssured, or similar
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SOAP API: SoapUI, or similar
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Test orchestration: CI/CD pipeline integration
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Reporting: Test results aggregation and trending
Test Types:
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Smoke tests - Critical path validation
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Regression tests - Full API coverage
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Contract tests - API specification compliance
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Performance tests - API response time validation
2.5. Security Testing
2.5.1. Authentication and Authorization
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TR-069 authentication modes (none, basic, digest)
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API authentication mechanisms
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Session management
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Role-based access control (if applicable)
2.6. Performance Monitoring
2.6.1. Grafana Dashboards
Required Dashboards:
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System Metrics
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CPU, memory, disk I/O
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JVM heap and garbage collection
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Thread count and states
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Network throughput
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Application Metrics
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Request rates per protocol
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Response time percentiles (p50, p95, p99)
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Error rates
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Active device connections
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Database Metrics
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Query execution time
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Connection pool utilization
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Transaction rates
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Slow query identification
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Cache Metrics
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Hazelcast hit/miss ratios
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Cache size and eviction rates
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Cluster health
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QoE Metrics
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Data ingestion rates
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ClickHouse write performance
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Storage utilization
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2.7. Failover and Recovery Testing
2.7.1. High Availability Scenarios
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ACS instance failure
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Graceful shutdown of one instance
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Ungraceful termination (kill -9)
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Device session continuity
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Request routing to surviving instance
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Database failover
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Primary database failure
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Switchover to standby
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Application reconnection
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Data consistency validation
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Hazelcast node failure
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Single node failure
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Cluster rebalancing
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Cache data recovery
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Performance impact
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3. Test Execution Plan
3.1. Phase 1: Deployment Validation (Week 1)
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Linux Docker deployment
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Linux manual deployment
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Windows manual deployment
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Error scenario documentation
3.2. Phase 2: Functional Testing (Week 2-3)
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Device registration and provisioning
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Windstream flows
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Update group operations
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QoE functionality
3.3. Phase 3: Load Testing (Week 4-5)
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Device load tests
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Update group load tests
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QoE load tests (both modes)
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Performance comparison with previous version
4. Test Deliverables
4.1. Documentation
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Test plan (this document)
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Test case specifications
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Test execution results
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Performance comparison report
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Troubleshooting guide
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Known issues and limitations
5. Success Criteria
5.1. Functional Requirements
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All device lifecycle operations successful
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100% Windstream flow validation pass rate
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Update group operations on 10,000+ devices
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QoE data integrity validated
5.2. Performance Requirements
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90%+ thread reduction vs. previous version confirmed
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Inform response time < 5 seconds (100% compliance)
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Other requests < 30 seconds (100% compliance)
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QoE ingestion order of magnitude faster
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Update group performance improvement demonstrated