Challenges
The organization must deliver frequent technology releases with zero production failures while ensuring seamless integration across sensors, gateways, and cloud systems. Strong compliance with ISO 9001, IEC 61508, and NERC-CIP standards demands reliable processes and audit-ready documentation.
Release Reliability
User Delivering frequent technology releases (new monitoring modules, analytics features, grid-automation updates) while ensuring zero critical failures in production.
System Integration
Integrating disparate systems (field sensors → edge gateways → cloud analytics → enterprise workloads) and verifying data integrity and workflow consistency.
Regulatory Compliance
Meeting industry standards (ISO 9001, IEC 61508, NERC-CIP) and maintaining audit-ready documentation of quality activities.
Testing Scalability
Scaling testing efforts globally across multiple business units, platforms and geographies without linear cost escalation.
Quality Automation
Shifting from manual testing at the end of development to proactive, continuous quality built into the pipeline.
Approaches
The approach built a scalable, automation-driven QA framework through assessment, modern tooling, and Agile integration. By embedding QA into development and leveraging continuous automation, the organization enhanced reliability, compliance, and continuous improvement.
Assessment & Strategy
- Conducted a comprehensive audit of existing test practices, tools, defect trends and release metrics.
- Defined a QA roadmap aligned with GE’s digital transformation goals: increase automation, shift-left testing, integrate QA in CI/CD.
- Identified key business-critical systems (e.g., grid control module, turbine analytics, maintenance ERP) to prioritise quality efforts.
Tooling & Process Implementation
- Introduced test management via Zephyr, defect/issue tracking via Jira, documentation via Confluence/SharePoint
- Adopted API testing tools (Postman) and performance monitoring tools (Dynatrace) to validate service-level reliability and performance.
- Built automated regression suites and integrated them into Jenkins pipelines for continuous test execution.
Operational Integration
- Embedded QA engineers into Agile squads working on development of modules, ensuring participation in daily stand-ups, sprint planning and retrospectives.
- Collaborated with operations and engineering teams to define Acceptance Criteria, traceability matrices and UAT workflows.
Critical Test Case Framework
- Identified “critical” workflows (e.g., real-time sensor ingestion → analytics → display to operator; ERP billing integration) and defined smoke & regression test suites.
- Prioritised test coverage on high-risk areas to protect data integrity, safety and continuity.
Continuous Improvement
- Set up dashboards to monitor key metrics (Defect Detection Rate, Automation Coverage, Release Stability Index) and drive decision making.
- Conducted retrospectives and root-cause-analysis of defects to feed improvement back into development and QA processes.
Knowledge Sharing & Governance
- Established a centralized QA knowledge base with best practices, templates, and reusable test assets.
- Conducted regular training sessions and cross-team reviews to ensure consistency and upskilling across global QA teams.
- Implemented governance checkpoints to maintain standardization and alignment with enterprise quality objectives.
Results
90%
Defect Detection Rate before production improved to approximately 90% of critical defects (vs ~60% previously).
70%
Automation Coverage of regression tests reached ~70%, leading to a 30% reduction in test cycle time and accelerated release cadence.
01%
Release Stability Index (critical post-deployment issues) dropped to <1 % of releases, enabling more predictable, reliable deployments.
35%
Maintenance & Rework Costs reduced by up to ~35% within the first 12-18 months, through fewer hot-fixes, roll-backs and downtime events.
40%
Test Execution Efficiency increased by 40%, driven by parallel test runs and optimized CI/CD pipelines, resulting in faster validation across global environments.
25%
Team Productivity improved by 25%, as automation and integrated workflows reduced manual effort and freed QA engineers to focus on high-value exploratory testing and analysis.
Future Outlook
Transforming traditional design with intelligent, adaptive, and future-focused experiences.
Advanced Test Automation Expansion
Further increase automation coverage beyond 80%, incorporating robotic process automation (RPA) and AI-driven defect prediction to accelerate validation and reduce manual dependencies.
Enhanced Performance Load Testing
Expand performance and load testing frameworks to accommodate emerging IoT and edge-data workloads, ensuring resilience and stability under real-world conditions.
Predictive Quality Analytics Utilization
Leverage data-driven insights and machine learning from QA metrics to predict potential failures early, enabling proactive reliability and system optimization.
Embedded DevOps Quality Integration
Deepen QA integration within DevOps and shift-left models, embedding “Quality by Design” principles across every development stage for continuous, built-in assurance.
Global QA Standardization
Establish a unified quality framework across all business units and geographies. This ensures consistent testing practices, shared tools, and standardized reporting for improved global visibility and governance.
AI-Driven Quality Insights
Integrate advanced AI and machine learning models to identify defect patterns, predict system failures, and recommend corrective actions before issues impact production.
Continuous Security Validation
Embed automated security testing and compliance checks within pipelines. This enables real-time vulnerability detection and strengthens protection against evolving cyber threats in digital and IoT environments.
Sustainable Quality Engineering
Adopt energy-efficient testing infrastructure and green data practices. This aligns QA operations with sustainability goals, reducing the environmental footprint of large-scale test environments.
Intelligent Test Orchestration
Implement smart orchestration engines that dynamically select, execute, and optimize test cases based on code changes, risk levels, and historical defect data to boost efficiency.
Cloud-Native QA Expansion
Migrate QA infrastructure to cloud-native environments, enabling on-demand scalability, faster environment provisioning, and seamless integration with distributed development teams.
Cross-Platform Validation Framework
Develop a unified validation framework supporting web, mobile, edge, and IoT platforms to ensure consistent performance and interoperability across all technology layers
Data-Driven Decision Governance
Use centralized QA analytics dashboards and KPIs to guide leadership decisions, prioritize improvements, and align quality initiatives with broader business objectives.
User Research
User research involved surveys and interviews with Lucentum Alicante fans, revealing a need for a centralized platform. Fans prioritized access to match details, tickets, and exclusive content. Personalized features and loyalty programs were key desires for enhancing engagement.
Company Name
NBA App
Euro League Basketball App
HoopMetrics
Lucentum Basketball Fans App
Company Info
Official platform for global basketball fans.
Hub for European basketball enthusiasts.
Advanced analytics for basketball strategy.
Lucentum fans' hub: matches, tickets, merchandise.
Match Details
Loyalty Programs
Personalized Content
Ticket Booking
AI-Powered Assistance
Merchandise
Subscriptions
User Persona
Name:
Michael Thompson
Age:
42
EDUCATION:
Master’s in Electrical and Computer Engineering
Job:
QA Director, General Electric Energy
Location:
Atlanta, USA
HOBBIES:
Data analytics, hiking, mentoring engineers
Bio
Michael is a seasoned QA leader driving digital quality transformation across GE Energy divisions. With 15+ years in testing and reliability engineering, he ensures energy systems from turbine monitoring to cloud analytics meet top performance and compliance standards.
Personality
Analytical
Detail-oriented
Strategic
Collaborative
Pain Points
Fragmented QA processes across different teams and geographies.
Manual test execution slowing down release cycles
Difficulty in measuring QA effectiveness through unified metrics
Goal
Achieve 80% test automation coverage.
Standardize QA tools and processes across all divisions.
Reduce post-production defects and downtime.
Ensure all systems meet ISO and IEC compliance standards
User Persona
Name:
Priya Nair
Age:
29
EDUCATION:
Bachelor’s in Computer Science
Job:
Senior QA Engineer (Automation Lead)
Location:
Barcelona, Spain
HOBBIES:
Playing badminton, reading tech blogs, volunteering for STEM education
Bio
Priya is an automation-focused QA engineer responsible for creating and maintaining regression and API test suites. She collaborates closely with developers and product owners to ensure new features are validated early in the sprint. She’s passionate about CI/CD, test automation frameworks, and improving release reliability through data-driven testing.
Personality
Proactive
Curious
Tech-savvy
Collaborative
Pain Points
Inconsistent test data and environments affecting test reliability
Lack of visibility into defect trends across teams.
Time-consuming manual validations for edge workflows.
Difficulty ensuring automation scripts remain aligned with changing requirements.
Goal
Increase test automation efficiency and maintainability
Integrate automated tests fully into Jenkins CI/CD pipelines
Improve collaboration between QA and DevOps teams
Contribute to predictive analytics for early defect detection
User Persona
Name:
Carlos Mendes
Age:
45
EDUCATION:
MBA in Operations Management
Job:
Product Manager Digital Energy Platforms
Location:
Lisbon, Portugal
HOBBIES:
Cycling, energy innovation podcasts, travel
Bio
Carlos manages GE’s digital energy analytics platform, ensuring reliable, compliant, and customer-focused updates. He bridges business and technology, using QA insights to drive measurable quality improvements and transparent release performance.
Personality
Strategic
Results-driven
Communicative
Empathetic
Pain Points
Limited real-time insight into release readiness
Difficulty correlating QA metrics to business outcomes
Bottlenecks in UAT signoffs delaying product launches
Inconsistent feedback loops between QA and product teams
Goal
Strengthen release predictability and customer confidence
Use QA metrics to support business decision-making
Align QA outcomes with product KPIs and reliability goals
Foster collaboration between product, QA, and engineering teams
User Journey Map
Persona: Michael Thompson (QA Director)
Actions
Action 1
Action 2
Action 3
Action 4
Task List
Review existing QA processes across divisions
Evaluate new automation tools
Approve QA strategy roadmap
Monitor QA performance dashboards
Feeling
Concerned about fragmented processes
Encouraged by automation progress
Confident in new QA roadmap
Satisfied with performance improvements
Thoughts
We need a unified QA framework across all regions
Automation will improve release quality and speed
This roadmap finally aligns with business goals
Our QA metrics are showing real ROI.
Improvement Opportunities
Establish global QA standards
Integrate automation tools across all teams
Improve traceability between QA and Dev
Implement AI-driven defect prediction models
Actions : 1
Task List
Review existing QA processes across divisions
Feeling
Concerned about fragmented processes
Thoughts
We need a unified QA framework across all regions.
Improvement Opportunities
Establish global QA standards
Actions : 2
Task List
Evaluate new automation tools
Feeling
Encouraged by automation progress
Thoughts
Automation will improve release quality and speed
Improvement Opportunities
Integrate automation tools across all teams
Actions : 3
Task List
Approve QA strategy roadmap
Feeling
Confident in new QA roadmap
Thoughts
This roadmap finally aligns with business goals.
Improvement Opportunities
Improve traceability between QA and Dev
Actions : 4
Task List
Monitor QA performance dashboards
Feeling
Satisfied with performance improvements
Thoughts
Our QA metrics are showing real ROI.
Improvement Opportunities
Implement AI-driven defect prediction models
User Journey Map
Persona: Priya Nair (Senior QA Engineer)
Actions
Action 1
Action 2
Action 3
Action 4
Task List
Design new test automation scripts
Execute regression and API tests
Analyze defect reports
Optimize CI/CD test pipeline
Feeling
Curious and motivated to innovate
Stressed under tight deadlines
Proud after finding critical defects
Confident in process improvement
Thoughts
Can we make tests reusable for multiple projects?
Need better data sets for consistent test results.
Glad I caught that defect before release.
Automation in CI/CD saves so much time
Improvement Opportunities
Provide stable test environments
Centralize defect analytics dashboards
Improve collaboration with QA
Automate more regression and API tests
Actions : 1
Task List
Design new test automation scripts
Feeling
Curious and motivated to innovate
Thoughts
Can we make tests reusable for multiple projects?
Improvement Opportunities
Provide stable test environments
Actions : 2
Task List
Execute regression and API tests
Feeling
Stressed under tight deadlines
Thoughts
Need better data sets for consistent test results.
Improvement Opportunities
Centralize defect analytics dashboards
Actions : 3
Task List
Analyze defect reports
Feeling
Proud after finding critical defects
Thoughts
Glad I caught that defect before release
Improvement Opportunities
Improve collaboration between QA and DevOps
Actions : 4
Task List
Optimize CI/CD test pipeline
Feeling
Confident in process improvement
Thoughts
Automation in CI/CD saves so much time.
Improvement Opportunities
Automate more regression and API tests
User Journey Map
Persona: Carlos Mendes (Product Manager – Digital Energy Platforms)
Actions
Action 1
Action 2
Action 3
Action 4
Task List
Review QA reports for feature releases
Plan next release cycle with QA input
Attend UAT sign-off meetings
Track post-release stability metrics
Feeling
Curious about product readiness
Reassured by QA feedback
Confident in launch
Proud of improved release stability
Thoughts
I need QA insights earlier in the product cycle.
“The release looks stable — fewer last-minute issues.”
“QA collaboration really improves customer trust.”
“Post-release data proves our quality improvements.”
Improvement Opportunities
Embed QA in product planning from the start
Use QA metrics for go/no-go decisions
Enhance communication during UAT
Implement dashboards linking QA results to business KPIs
Actions : 1
Task List
Review QA reports for feature releases
Feeling
Curious about product readiness
Thoughts
I need QA insights earlier in the product cycle
Improvement Opportunities
Embed QA in product planning from the start
Actions : 2
Task List
Plan next release cycle with QA input
Feeling
Reassured by QA feedback
Thoughts
The release looks stable fewer last-minute issues.
Improvement Opportunities
Use QA metrics for go/no-go decisions
Actions : 3
Task List
Attend UAT sign-off meetings
Feeling
Confident in launch
Thoughts
QA collaboration really improves customer trust.
Improvement Opportunities
Enhance communication during UAT
Actions : 4
Task List
Track post-release stability metrics
Feeling
Proud of improved release stability
Thoughts
Post-release data proves our quality improvements.
Improvement Opportunities
Implement dashboards linking QA results to business KPIs
Key Takeaways
The QA Transformation at General Electric Energy unified and automated quality processes across global systems. By embedding QA into Agile and DevOps, GE shifted from reactive testing to continuous quality assurance, improving reliability, compliance, and speed.
Automation, standardized tools, and real-time dashboards reduced defects, cut costs, and enhanced release stability — making GE’s digital platforms more efficient, predictable, and audit-ready
