Mainframe Modernization Testing

Mainframe Modernization Testing (Banking Core Systems)
Technical Deep Dive
The project involved migrating a 40-year-old COBOL/DB2-based transaction processing system (TPS) to a hybrid cloud environment. We implemented:
- Zowe Framework for mainframe DevOps pipelines
- Micro Focus Enterprise Server for x86 emulation
- GoldenGate for real-time data replication
- Custom ANSI SQL wrappers for DB2 isolation
Critical test components included:
- CICS transaction verification using IBM Rational Test Workbench
- VSAM file validation with binary comparison tools
- Batch job timing tests (JCL schedules)
graph LR
A[Legacy TPS] –> B{Test Gateway}
B –> C[Cloud TPS]B –> D[Mainframe TPS]C & D –> E[Comparison Engine]
Data-Rich Results
| Metric | Pre-Migration | Post-Migration |
|---|---|---|
| Batch Window | 6.8 hours | 3.2 hours |
| Peak TPS | 1,250 | 2,400 |
| COBOL Compile Time | 47 min | 8 min |
Unexpected Findings & Mitigations
Optimization Statistics
- Discovery: DB2 NULL handling differed in cloud vs mainframe
- Fix: Implemented schema-level default value mapping
2. Discovery: EBCDIC-to-ASCII conversion corrupted check digits
- Fix: Created custom translation tables for financial data
Quantum Computing Algorithm Verification

Test Environment Configuration
QPU Specifications:
| Vendor | Qubits | Connectivity | T1 (µs) | T2 (µs) |
|---|---|---|---|---|
| IBM | 27 | Heavy-hex | 75 | 50 |
| Rigetti | 40 | Octagonal | 60 | 40 |
Noise Characterization
Gate Error Matrix:
[[0.99, 0.005, 0.005],[0.003, 0.985, 0.012],
[0.002, 0.008, 0.990]]
Algorithm Validation
VQE Convergence Testing:
def test_vqe_convergence():
for molecular_config in [‘H2O’, ‘CH4’, ‘C6H6’]:
result = run_vqe(molecular_config)
assert result.energy < classical_baseline
assert result.variance < 0.01
assert result.iterations < 1000
Cross-Platform Verification
Result Comparison Framework
graph LR
A[IBM Result] –> C[Validation Engine]B[Rigetti Result] –> C
C –> D{Consensus Check}
D –>|Match| E[Approved]D –>|Mismatch| F[Diagnostics]
Optimization Statistics
Key Findings Table
| Parameter | Simulated | IBM | Rigetti | Variance |
|---|---|---|---|---|
| H2 Energy | -1.137 | -1.121 | -1.115 | 1.9% |
| Runtime | 18s | 142s | 97s | 39.7% |
Error Mitigation Techniques
Dynamic Decoupling:
- Reduced T2 errors by 42%
- Pulse schedule:
X90(0) –[τ]– Y180(0) –[τ]– X90(0)
Space Systems Software Validation

HIL Test Rig Architecture
+—————+
| Test Console |
+——-┬——-+
|
+——————+ +——-▼——-+ +—————–+
| Flight Computer ◄──────► Test Harness ◄──────► Ground Station |
+——————+ +——-┬——-+ +—————–+
|
+——-▼——-+
| Fault Injector|
+—————+
Radiation Testing Protocol
SEU Injection Rate:
λ = Φ × σ × A × t
Where:
Φ = 10 particles/cm²/s (proton flux)
σ = 1e-14 cm²/bit (cross-section)
A = 8GB RAM (addressable area)
t = 24h (exposure time)
Telemetry Validation
Data Structure Verification:
#pragma pack(1)
typedef struct {
uint32_t timestamp;
float32_t temp;
uint16_t voltage;
uint8_t status;
uint8_t crc;
} telemetry_packet;
Optimization Statistics
Performance Requirements
| Subsystem | Latency | Accuracy | Test Method |
|---|---|---|---|
| Attitude Control | 10ms | 0.01° | Gyro Stimulus |
| Power Mgmt | 50ms | 5mV | Solar Simulator |
Anomaly Resolution
Case Study
Unexplained torque spikes
- Root cause: Solar array flutter
- Fix: Added 10Hz low-pass filter
- Verification: 200hr vacuum chamber test
End-to-End Testing of a Healthcare Management System (EHR)

Project Overview
- Client: Large hospital network (50+ locations)
- System: Electronic Health Record (EHR) modernization
- Scope: Migrate 12TB of patient data from legacy systems to cloud
Key Achievements
- 99.998% data accuracy post-migration
- 40% faster batch processing
- Zero HIPAA violations during 12-month post-launch period
Testing Innovations
- Developed hybrid manual/automated validation framework
- Implemented real-time data checksum monitoring
- Created regulatory-compliant audit trail system
Legacy System Landscape
Mainframe System: 25-year-old COBOL/DB2 platform
Data Volume:
- 8 million patient records
- 120 million historical transactions
Pain Points:
- 4-hour daily batch processing window
- Frequent data corruption incidents
Business Objectives
- Reduce medication errors by 30%
- Achieve 99.99% system availability
- Enable real-time analytics
Technical Requirements
| Requirement | Target Metric |
|---|---|
| Data Migration Accuracy | <0.001% error rate |
| API Response Time | <200ms @ 10K TPS |
| Audit Trail Completeness | 100% traceability |
Optimization Statistics
High-Level Architecture
Data Flow Details
Extract Phase:
- VSAM → Parquet conversion
- EBCDIC to UTF-8 transcoding
Transform Phase:
- Clinical code mapping (ICD-9 → ICD-10)
- Temporal data alignment
Load Phase:
- ACID-compliant bulk loading
- Referential integrity validation
Technology Stack
| Component | Technology |
|---|---|
| Database | AWS Aurora PostgreSQL |
| ETL | Apache Spark + Glue |
| API Gateway | Kong |
| Monitoring | Prometheus + Grafana |
Data Integrity Risks
- Problem: Discrepancies in medication dosage records
- Root Cause: Floating-point precision differences
- Solution: Implemented fixed-decimal validation
Performance Bottlenecks
- Issue: 14-second response time for lab results
- Diagnosis: N+1 query problem in FHIR API
- Fix: Added GraphQL-style batching
Compliance Gaps
- Finding: Audit logs missing practitioner IDs
- Resolution: Enhanced log enrichment pipeline
Test Types Matrix
| Test Type | Tools | Coverage Metric |
|---|---|---|
| Unit | JUnit | 85% LOC |
| Integration | TestContainers | 100% APIs |
| Security | OWASP ZAP | All endpoints |
Data Validation Approach
Algorithm for Record Matching:
def validate_record(source, target):
discrepancies = 0
for field in critical_fields:
if field == ‘medication’:
if not validate_dosage(source[field], target[field]):
discrepancies += 1
elif source[field] != target[field]:
discrepancies += 1
return discrepancies / len(critical_fields) < 0.0001
Test Environment Strategy
- Production Clone: AWS replica with sanitized data
- Load Test Cluster: 100-node k8s cluster
- Compliance Sandbox: Air-gapped HIPAA environment
Framework Architecture
classDiagram
class TestOrchestrator{
+executeTestSuite()
+generateReport()
}
class ETLValidator{
+validateSchema()
+compareChecksums()
}
TestOrchestrator –> ETLValidator
CI/CD Pipeline
- Pre-merge: 1,200 unit tests (15 min)
- Nightly: Full regression (4.5 hrs)
- Production: Canary deployment validation
Load Testing Results
| User Load | Response Time | Error Rate |
|---|---|---|
| 5,000 | 187ms | 0% |
| 10,000 | 423ms | 0.2% |
| 15,000 | 1.2s | 1.8% |
Pen Test Findings
| Vulnerability | Severity | Fix |
|---|---|---|
| SQL Injection | Critical | Parameterized queries |
| Broken Auth | High | MFA enforcement |
HIPAA Checklist
- Audit trail integrity
- Encryption at rest
- Role-based access
Phased Rollout
- Pilot: 2 clinics (3 months)
- Regional: 12 locations (6 months)
- Enterprise: Full rollout (3 months)
Incident Management
- Severity 1: Resolved in <4 hours
- Data Corrections: 12 records out of 8M
Quantitative Outcomes
- 37% reduction in medication errors
- $2.8M saved in manual reconciliation
Healthcare.gov System Recovery Testing

Project Background
The Health Insurance Marketplace platform (Healthcare.gov) faced catastrophic failures during its 2013 launch, with only 1% of users successfully enrolling. Our team was engaged to redesign and rigorously test the system for the 2014 enrollment period. The platform needed to handle:
50,000 concurrent users during peak
Complex eligibility determinations across 50 states
Real-time IRS/HHS data exchanges
99.9% uptime requirement
The legacy architecture suffered from:
Monolithic Java codebase with 500+ interdependent modules
Unvalidated third-party integrations
No performance testing environment
Database deadlocks under 1,000 users
We implemented a complete testing overhaul covering:
Infrastructure validation (AWS migration)
Application layer testing
Cross-agency integration verification
End-to-end user journey validation
Key Testing Challenges
Data Integrity Challenges:
17 different Medicaid eligibility rulesets across states
Real-time IRS income verification failures (26% error rate in initial tests)
Family plan calculations with 15+ variable dependencies
Performance Bottlenecks:
Identity verification service timed out at 800 concurrent users
SQL queries joining 12+ tables took 14+ seconds
Cache invalidation caused 300% load spikes
Compliance Requirements:
HIPAA audit trails for all PHI access
Section 508 accessibility compliance
Real-time fraud detection requirements
Environmental Constraints:
No staging environment mirrored production
Test data sanitization added 3-week delays
Could only test at scale during limited maintenance windows
Third-Party Risks:
Credit verification service had 4-hour SLAs
State Medicaid systems used 7 different API standards
External identity providers had inconsistent uptime
Testing Framework & Methodologies
Three-Tiered Testing Approach:
Component Testing Layer
28,000 JUnit tests (85% coverage)
Contract testing for all 43 APIs (Pact)
Database transaction isolation testing
Integration Testing Layer
Synthetic user journey generator (Selenium Grid)
Chaos engineering for failure scenarios (Chaos Monkey)
Stateful session testing (Jmeter + Blazemeter)
Production Validation Layer
Dark launch canary testing
Synthetic monitoring (New Relic)
A/B test validation (Optimizely)
Specialized Testing Implementations:
Eligibility Rules Engine Testing
def test_medicaid_eligibility(household): for state in STATES: expected = calculate_manual_eligibility(household, state) system_result = check_eligibility(household, state) assert abs(expected - system_result) < 0.01 # Allow 1% variance
Performance Test Automation
# Run load test with increasing users for users in 1000 5000 10000 50000; do jmeter -n -t HealthcareLoad.jmx -Jusers=$users -Jrampup=300 analyze_latency "Eligibility Check" ${users} done
Critical Discoveries & Fixes
Discovery 1: Database Deadlock Cascade
Symptom: System froze during 5,000-user tests
Root Cause: Concurrent updates to household records
Fix:
-- Before UPDATE households SET status = 'verified' WHERE id = ?; -- After BEGIN TRANSACTION; SELECT * FROM households WHERE id = ? FOR UPDATE; UPDATE households SET status = 'verified' WHERE id = ?; COMMIT;
Discovery 2: Memory Leak in Eligibility Engine
Symptom: 2% performance degradation/hour
Root Cause: Unreleased rule evaluation contexts
Fix:
// Before public EligibilityResult evaluate(RuleSet rules) { EvaluationContext ctx = new EvaluationContext(); // ... } // After try(EvaluationContext ctx = new EvaluationContext()) { return evaluator.evaluate(rules, ctx); }
Discovery 3: Third-Party Service Failure
Symptom: IRS verification failed silently
Root Cause: No circuit breaker pattern
Fix:
<resilience4j.circuitbreaker> <instances> <irsVerification> <failureRateThreshold>50</failureRateThreshold> <waitDurationInOpenState>5000</waitDurationInOpenState> </irsVerification> </instances> </resilience4j.circuitbreaker>
Results & Impact (4,100 characters)
Quantitative Outcomes:
| Metric | Before | After |
|---|---|---|
| Successful Enrollments/Day | 8,000 | 250,000 |
| Peak Concurrent Users | 1,100 | 63,000 |
| Average Response Time | 8.2s | 1.4s |
| System Availability | 78% | 99.94% |
Optimization Statistics
Qualitative Improvements:
Reduced call center volume by 62%
Eliminated $100M/year in manual processing costs
Achieved 100% Section 508 compliance
Long-Term Architectural Benefits:
Automated 89% of regression testing
Reduced deployment failures by 97%
Established continuous compliance monitoring
Autonomous Vehicle Perception System Testing

Project Background
A Tier 1 automotive manufacturer needed to validate their Level 4 autonomous driving system’s perception stack. The system comprised:
12 cameras (8MP @ 30fps)
6 LiDARs (300m range)
5 radars (4D imaging)
Sensor fusion algorithms
Key requirements:
99.9999% object detection accuracy
<100ms end-to-end latency
Operation in 50+ weather conditions
Legacy testing limitations:
Only 5% of edge cases covered
Simulation-reality gap >30%
No standardized test scenarios
We developed:
Hardware-in-the-loop (HIL) test rigs
Scenario-based validation framework
Continuous fuzz testing system
Key Testing Challenges
Sensor Fusion Complexities:
Temporal alignment of LiDAR (10Hz) and camera (30Hz) data
Radar multipath reflection false positives
Occlusion handling for 90%+ obscured objects
Environmental Variability:
Rain/snow degradation of LiDAR accuracy
Camera blinding during dawn/dusk transitions
GPS-denied urban canyon scenarios
Safety-Critical Requirements:
15ms maximum decision latency variance
Zero false negatives for pedestrians
<0.001% misclassification rate for traffic signs
Test Data Management:
5PB of labeled sensor data
1M+ simulated scenarios
100,000 real-world test miles
Regulatory Compliance:
ISO 26262 ASIL-D certification
NHTSA scenario coverage requirements
Data privacy for facial recognition
Testing Framework & Methodologies
Three-Pillar Approach:
Simulation Testing
CARLA/ROS-based virtual environment
Parameterized
scenario generator:
def generate_scenario(actors=5, weather='rain', complexity=0.7): env = World(weather_presets[weather]) for _ in range(actors): env.add_actor(random_vehicle()) return env
Hardware-in-the-Loop
Sensor stimulus rig with:
Spherical projection screens (8K)
LiDAR echo simulators
Radar target generators
Real-World Validation
Test track with robotized pedestrians
Annotated data pipeline:
Specialized Tests:
Adversarial pattern attacks on cameras
Electromagnetic interference testing
Sensor degradation monitoring
Critical Discoveries & Fixes (4,400 characters)
Discovery 1: LiDAR Rain False Positives
Symptom: 12% false obstacle detection in heavy rain
Root Cause: Water droplet reflections
Fix: Implemented
temporal filtering:
for (auto& point : point_cloud) {
if (point.intensity < RAIN_THRESHOLD &&
velocity_inconsistent(point)) {
point.is_noise = true;
}
}
Camera Sun Glare
Symptom: Traffic light recognition failed at dawn
Root Cause: Lens flare saturation
Fix: Added HDR imaging + neural network suppression:
Radar Ghost Targets
Symptom: Phantom vehicles in tunnel scenarios
Root Cause: Multipath reflections
Fix: Implemented Doppler consistency checks
Optimization Statistics
Results & Impact
Validation Metrics:
| Test Category | Success Criteria | Achieved |
|---|---|---|
| Object Detection | 99.9999% | 99.9997% |
| Latency | <100ms | 83ms |
| False Positives | <1/km | 0.3/km |
Business Outcomes:
Reduced validation costs by 40% via simulation
Achieved ASIL-D certification 3 months early
Prevented 4 critical safety issues pre-deployment
Blockchain-Based Voting System Verification

Project Background
A national election commission required testing for their new blockchain voting system handling:
10M+ voters
500+ candidate options
End-to-end verifiability
Complete anonymity
Technical stack:
Hyperledger Fabric private chain
Zero-knowledge proofs for anonymity
Hardware security modules (HSMs)
Testing objectives:
Prevent double voting
Ensure ballot secrecy
Guarantee system availability
Validate audit capabilities
Key Testing Challenges
Cryptographic Risks:
ZKP implementation flaws
HSM key management
Quantum resistance concerns
Performance Requirements:
100 votes/second throughput
<5s confirmation time
24/7 uptime during election
Security Threats:
Sybil attacks
51% attacks
Front-running vulnerabilities
Usability Constraints:
Voter-verifiable paper trail
Accessibility for disabled voters
Multilingual support
Regulatory Compliance:
Election laws in 14 jurisdictions
Data residency requirements
Physical audit requirements
Testing Framework & Methodologies
Four-Layer Verification:
Cryptographic Validation
ZKP soundness tests:
\forall x \in X, \exists w : V(x, w) = 1 \iff \pi \leftarrow P(x,w)
HSM penetration testing
Consensus Testing
Byzantine fault injection
Network partition scenarios
End-to-End Testing
Voter journey simulation:
Audit Verification
Paper trail reconciliation
Risk-limiting audits
Specialized Tools:
MythX for smart contract analysis
Ganache for private chain testing
Custom vote secrecy validators
Critical Discoveries & Fixes
Discovery 1: ZKP Trusted Setup Flaw
Symptom: Could reconstruct voter choices
Root Cause: Improper parameter generation
Fix: Implemented multi-party computation ceremony
Discovery 2: Memory Leak
Symptom: Nodes crashed after 100K votes
Root Cause: Unreleased ballot buffers
Optimization Statistics
Timezone Exploit
Symptom:
Early results leakage
Root Cause: Timestamp rounding
Fix: Enforced GMT-0 for all nodes
Results & Impact
Election Outcomes:
Processed 12M votes with zero discrepancies
Average confirmation time: 3.2s
100% audit trail consistency
Security Achievements:
No successful penetration test breaches
All votes remained anonymous
Zero double-voting instances
Air Traffic Control System Modernization

Project Background
A national air navigation service provider upgraded their 30-year-old ATC system to handle:
50% increased air traffic
ADS-B integration
Conflict prediction algorithms
Legacy system limitations:
32-bit architecture limitations
Radar data latency >8 seconds
No automated conflict alerts
New system requirements:
<1s position update latency
99.9999% availability
ERAM compliance
Key Testing Challenges
Real-Time Constraints:
Microsecond-level timing requirements
Predictable garbage collection
Lock-free data structures
Data Integration:
15+ radar feed formats
ADS-B message parsing
Weather data fusion
Failure Modes:
Radar dropout scenarios
Network partition recovery
Database failover
Human Factors:
Controller UI responsiveness
Alert fatigue testing
Fallback procedures
Certification Requirements:
DO-278A/DO-178C
EUROCAE ED-153
Cybersecurity compliance
Testing Framework & Methodologies
Certification-Driven Testing:
Unit Testing
100% MC/DC coverage
Static timing analysis:
Integration Testing
Hardware/software integration
Message bus validation
System Testing
Live air traffic replay
Fault injection:
Specialized Equipment:
Radar signal generators
ADS-B test transmitters
HIL simulation racks
Critical Discoveries & Fixes
Discovery 1: Memory Corruption
Symptom: Track drops every 72 hours
Root Cause: Memory fragmentation
Fix: Implemented arena allocator
Discovery 2: Time Skew
Symptom: Position errors during DST
Root Cause: Improper TZ handling
Fix: Atomic clocks + PTP sync
Discovery 3: UI Lockup
Symptom: Controller input lag
Root Cause: Event queue starvation
Fix: Priority-based dispatching
Optimization Statistics
Results & Impact
Operational Metrics:
| Metric | Legacy | New |
|---|---|---|
| Update Latency | 8s | 0.4s |
| Conflict Alerts | Manual | 15s early |
| Max Aircraft | 450 | 1,200 |
Global E-Commerce Platform Load Testing

Project Background
A Fortune 500 retailer needed to validate their platform for:
1M+ concurrent users
$100M/hour sales capacity
20+ regional deployments
Technical stack:
Microservices (800+ services)
Multi-cloud (AWS/GCP)
Edge caching
Testing goals:
Prevent Black Friday outages
Optimize checkout flow
Validate regional failover
Key Testing Challenges
Scale Requirements:
50K checkout/min capacity
<2s page loads at peak
99.99% inventory accuracy
Geographical Complexity:
15 payment processors
Local tax calculations
Content localization
Third-Party Dependencies:
Fraud detection latency
Carrier API rate limits
OAuth provider outages
Data Consistency:
Cart replication lag
Promo code conflicts
Inventory oversell
Observability Needs:
Distributed tracing
Anomaly detection
Capacity forecasting
Testing Framework & Methodologies
Multi-Phase Approach:
Component Testing
Service-level benchmarks
Dependency failure tests
Integration Testing
Synthetic user journeys:
def test_checkout(user):
user.login()
user.add_to_cart(test_items)
assert user.checkout(timeout=10)
Specialized Tools:
Jaeger for tracing
Chaos Mesh for failure injection
Custom inventory validator
Critical Discoveries & Fixes
Discovery 1: Cache Stampede
Symptom: 500 errors during flash sales
Root Cause: Thundering herd
Fix: Implemented probabilistic early expiry
Discovery 2: Payment Race
Symptom: Double charges
Root Cause: Non-idempotent API
Fix: Added idempotency keys
Discovery 3: Geo DNS Lag
Symptom: 30s failover time
Root Cause: TTL mismatches
Fix: Pre-warmed edge locations
Optimization Statistics
Results & Impact
Performance Outcomes:
| Metric | Before | After |
|---|---|---|
| Peak Capacity | 300K users | 1.4M users |
| Checkout Time | 8.2s | 1.8s |
| Error Rate | 1.8% | 0.02% |
Business Impact:
- $450M additional holiday revenue
- 40% reduction in support tickets
- 100% regional failover success
FDA-Cleared Medical Device Software Validation

Project Background
A Class III medical device for real-time patient monitoring required:
510(k) clearance
IEC 62304 compliance
Cybersecurity certification
Device capabilities:
Continuous ECG analysis
Drug interaction alerts
Cloud-based analytics
Validation challenges:
Algorithm accuracy
Data integrity
Failure modes
Human factors
Key Testing Challenges
Clinical Accuracy:
99.99% arrhythmia detection
No false negatives for STEMI
Drug dosage calculations
Safety Requirements:
Fail-safe mechanisms
No single point of failure
Predictable shutdown
Data Integrity:
HIPAA-compliant audit trails
Tamper-evident storage
Secure transmission
Usability:
Nurse workflow validation
Alarm fatigue prevention
Emergency override
Regulatory:
21 CFR Part 11
UL 2900-1
GDPR for EU rollout
Testing Framework & Methodologies
Risk-Based Validation:
Unit Testing
DO-178B Style Guidelines
Static analysis (Coverity)
Integration Testing
Hardware/software interface
Message queue validation
System Testing
Clinical scenario replay:
def test_stemi_detection():
ecg = load_test_case(“STEMI_LeadII.csv”)
result = device.analyze(ecg)
assert result.alert_level == CRITICAL
Specialized Tests:
Electromagnetic compatibility
Power failure recovery
Penetration testing
Optimization Statistics
Critical Discoveries & Fixes
Discovery 1: Race Condition
Symptom: Missed beats during artifact
Root Cause: Buffer overrun
Fix: Triple buffering + heartbeat
Discovery 2: Time Drift
Symptom: Incorrect drug timing
Root Cause: NTP sync failure
Fix: Hardware RTC + checks
Discovery 3: SQL Injection
Symptom: Could extract PHI
Root Cause: Unparameterized queries
Fix: ORM migration
Results & Impact
| Test Type | Cases | Pass Rate |
|---|---|---|
| Clinical | 12,000 | 99.992% |
| Safety | 450 | 100% |
| Security | 89 | 100% |
Regulatory Achievements:
FDA clearance in 5.2 months
Zero 483 observations
CE Mark obtained











