Introduction:
The landscape of software development has undergone a dramatic transformation, particularly since the widespread adoption of DevOps practices post-2016 and the accelerated digital shifts during and after the COVID-19 pandemic. In this era of rapid technological advancement, exemplified by the rise of GenAI and low-code/no-code platforms, the traditional Testing Center of Excellence (TCoE) is no longer sufficient. Organizations are now challenged to evolve towards a dynamic Quality Engineering Center of Excellence (QCoE) that integrates quality throughout the Software Development Life Cycle (SDLC).
This article will explore the critical elements involved in this transition, highlighting the key areas influencing the Quality Engineering space and providing a comparative analysis between TCoEs and modern QCoEs, ultimately guiding organizations towards establishing or transforming their quality assurance practices for future success.
You might be curious to know some of those key areas that have been influencing the Quality Engineering (QE) space in current times:
1. Advancement of GenAI
- Instant Test Case Generation: AI can generate test cases based on requirements and code coverage, improving efficiency and reducing human errors.
- Predictive Analytics: ML algorithms can analyze historical process data to predict potential risks and prioritize testing efforts.
- Intelligent Test Automation: AI-powered test automation frameworks can adapt to dynamically changing environments and handle complex scenarios for better reliability.
2. Integration of advanced automation tools with DevOps eco-system and CI/CD
- Shift-Left Testing: Testing is initiated and integrated earlier in the SDLC to detect defects sooner.
- Continuous Testing: Automated testing is smoothly integrated into the CI/CD pipeline to ensure quality for each build/deployment/release.
- Test Automation: Automation tools are used to execute tests as often as needed and efficiently.
3. Cloud-based Testing
- Testing in the Cloud: Cloud platforms provide scalable and flexible environments for testing various applications.
- Performance Testing: Cloud-based tools can simulate high loads and measure application performance.
- Security Testing: Cloud environments require specific security measures to protect sensitive data. You might be interested in learning more about Security Testing as discussed in an interesting article here in TAF website: https://testautomationforum.com/security-testing-a-shield-against-modern-cyber-threats/
4. Test Data Management
- Synthetic Data Generation: Creating realistic test data to simulate real-world scenarios.
- Data Masking: Protecting sensitive data while maintaining test data quality.
- Test Data Management Tools: Using specialized tools to manage and govern test data. Read more about Test data management using AI-powered synthetic data generators here: https://testautomationforum.com/test-data-management-using-ai-powered-synthetic-data-generators/
5. Mobile and IoT Testing
- Device Fragmentation: Testing on a wide range of devices and operating systems is essential using cloud-based solutions to configure and test against variety of configurations.
- Performance Optimization: Ensuring optimal performance on mobile and IoT devices.
- Security Testing: Protecting against vulnerabilities in mobile and IoT applications.
6. Emerging Technologies
- Blockchain Testing: Verifying the integrity and security of blockchain-based applications.
- Quantum Computing Testing: Evaluating the impact of quantum computing on software testing.
- Low-Code and No-Code Testing: Testing commercial products and enterprise applications using these advanced automation platforms.
These trends are shaping the future of Quality Engineering, emphasizing automation, integration, and the ability to adapt to rapidly changing technologies. Quality Engineers need to stay updated with these developments to ensure their organizations remain competitive and deliver high-quality software.
Testing CoE vs. Quality Engineering CoE (QCoE): A comparative analysis
If you were a part of Testing CoE as part of your career path, you might be knowing the manual gathering of metrics and creation of KPI data was often a laborious and frustrating process. However, QCoEs are now equipped with advanced analytics platforms and integrated testing tools, making these tasks far more efficient. Let’s discuss little more about the differences between traditional TCoEs and modern QCoEs?
Quality Assurance “assures” quality of product where Quality Engineering “drives” the development of quality product and process. While both Testing Center of Excellence (TCoE) and Quality Engineering Center of Excellence (QCoE) aim to improve software quality, they have distinct focuses and scopes.
The below table explains their respective primary Focus, Objective, Scope and other differences between TCoE and QCoE:
| Comparison between Testing CoE with QE CoE | ||
| Features | Testing CoE | Quality Engineering CoE |
| Primary focus | Testing activities (Manual & automation) | Quality throughout the SDLC |
| Scope | Testing phase | Entire SDLC |
| Main objective | Ensure quality standards | Prevent defects at the earliest in SDLC |
| Approach | Reactive to Proactive | Fully Proactive (Shift-Left) |
| Automation coverage | Good | Much higher |
| Cost of testing | Moderate | Much reduced cost due to higher level of integration, automation and early detection of defects |
| Ease of scalability | Good | Much flexible |
| Efficiency & productivity | High | Much higher |
| Reliability & Quality of Products/Apps delivered | Good | Superior |
| Collaboration between teams | Good | Very effective |
| “Go-to-Market” time | Good | Much faster |
| Cost savings | Good | High |
| Customer satisfaction | Good | Superior |
| “Shift-Left” adaptability | Not always | Consistent |
| Continuous improvement of processes | Moderate | High |
| Ability to support large and complex commercial products and Apps | Not always | With ease |
| Gen-AI adaptability | Limited | Very high |
| Resource allocation/Reusability | Good | Very high |
| DevOps/Continuous Testing abilities | Good | Superior |
| Measurement of success/Testing metrics | KPI Based (Limited to Technology and Process) | More granular metrics through advanced AI/analytics-based dashboards. (Across Technology, Process and Business) |
| Support for futuristics Tools/Platforms like Low-code, No-code tools | Good | Superior |
| Desired ROI to QA Organization | Good | Much quicker ROI |
| Metrics for Top Management | Good | Reliable Data for CXOs |
| Support for Emerging Technologies | Moderate | Superior |
Recommended steps for setting up a brand-new QCoE or transforming your TCoE into a modern QCoE
In today’s rapidly evolving technological landscape, organizations are increasingly recognizing the critical role of quality engineering (QE) in ensuring the success of their software products. A well-established Quality Engineering Center of Excellence (QCoE) is essential to drive innovation, improve customer satisfaction, and achieve competitive advantage.
Setting up a brand-new Quality Center of Excellence (QCoE) or transforming a traditional Testing Center of Excellence (TCoE) into a modern QCoE is a strategic move to elevate quality assurance (QA) into a proactive, value-driven engine.
Keep in mind that establishing a robust Quality Engineering Center of Excellence (QCoE) begins with a foundational budget (as in any IT initiative). This initial step dictates the scope, scale, and sustainability of the QCoE’s operations. A well-defined budget allows for strategic resource allocation, enabling informed decisions regarding technology investments, talent acquisition, and infrastructure development. Without a clear financial framework, organizations risk underfunding critical components, leading to compromised quality initiatives and ultimately, hindering the QCoE’s ability to deliver its intended value. Budgeting, therefore, acts as the cornerstone, ensuring that the QCoE is built on a solid financial footing, capable of adapting to evolving needs and driving long-term success.
Let’s discuss about the key steps that can be followed as part of establishing the QCoE.
- Define the Vision and Scope
- New QCoE: Start with a clear purpose—shift QA from a “bug-finding” cost center to a quality-driven profit enabler. Scope it to cover end-to-end quality: code, UX, performance, security, and customer satisfaction.
- Transforming TCoE: Assess the current TCoE’s gaps—likely heavy on manual testing, siloed teams, or outdated metrics. Redefine its mission to integrate modern QE (Quality Engineering) principles like automation, DevOps alignment, and business outcomes.
- Action: Draft a charter. Example: “Deliver 99% defect-free releases, 50% faster, with zero customer escalations.” Align it with C-suite goals (cost, speed, quality).
- Build a Lean, Skilled Core Team
- New QCoE: Hire or train a small, versatile crew—QE architects, automation engineers, and data analysts. Aim for T-shaped skills (broad knowledge, deep in one area).
- Transforming TCoE: Upskill existing testers into QE roles. Swap rigid manual testers for engineers fluent in tools like Selenium, Jenkins, or AI-driven testing platforms (e.g., Mabl). Retire legacy mindsets via workshops, roadshows etc.
- Action: Target 5-10 people initially. Example: 1 lead (champion/visionary), 2 automation experts, 1 performance guru, 1 metrics nerd.
- Embed Automation as the Backbone
- New QCoE: Design an automation-first framework from day one—unit tests, API testing, UI automation, and performance scripts. Pick scalable tools (e.g., Cypress, JMeter) and integrate with CI/CD pipelines.
- Transforming TCoE: Audit current manual processes; automate 70%+ of repetitive tasks within 6 months. Shift from script-heavy to script-less tools for speed.
- Action: Set a goal—e.g., “Automate 80% of regression tests by Q3.” Pilot with one project, then scale.
- Align with DevOps and Agile
- New QCoE: Build QA into the DevOps loop—continuous testing, not end-of-cycle checks. Embed QE engineers in sprint teams to catch issues early.
- Transforming TCoE: Break silos between dev, test, and ops. Replace waterfall handoffs with “shift-left” (early testing) and “shift-right” (post-prod monitoring) approaches.
- Action: Integrate testing into Jenkins/GitLab pipelines. Measure cycle time reduction (e.g., from 2 weeks to 2 days).
- Focus on Metrics That Matter
- New QCoE: Skip vanity metrics (test case count) for business-focused ones: defect escape rate, mean time to detect (MTTD), customer NPS tied to quality.
- Transforming TCoE: Ditch old-school pass/fail rates. Track automation ROI, release stability, and cost-per-defect. Use data to prove QE’s value to execs.
- Action: Build a dashboard (e.g., via Grafana or Power BI or other similar platform that you like) showing real-time quality KPIs. Target: <1% defects in prod.
- Leverage Advanced Tech (AI/ML, Touchless Testing)
- New QCoE: Start with AI-powered testing—self-healing scripts, anomaly detection, or predictive defect analysis. Think “touchless testing” (my favourite) for zero human intervention in routine checks.
- Transforming TCoE: Layer AI onto existing tools—e.g., use ML to prioritize test cases or flag high-risk code. Transition from manual oversight to autonomous QA.
- Action: Pilot an AI tool (e.g., Testim or Functionize) on a critical app; aim for 30% efficiency gain in 90 days.
- Establish Governance and Standards
- New QCoE: Set lightweight, enforceable QE standards—coding guidelines, test coverage thresholds (e.g., 80% unit, 60% UI), and security baselines.
- Transforming TCoE: Update outdated TCoE processes to modern QE norms—think ISTQB for QE or OWASP for security.
- Action: Publish a QE playbook; enforce via peer reviews and automated linting/checks.
- Drive a Quality Culture
- New QCoE: Make quality everyone’s job—devs write tests, PMs own UX bugs, leadership champions QE wins. Reward defect-free sprints.
- Transforming TCoE: Shift testers from gatekeepers to collaborators. Run hackathons or “bug bashes” to energize the team.
- Action: Launch a “Quality First” campaign—e.g., monthly shoutouts for top QE contributors.
- Scale with a Proof of Concept (PoC)
- New QCoE: Pick a high-visibility project (e.g., a client app or internal tool) to showcase QCoE’s value—fast delivery, zero prod issues.
- Transforming TCoE: Retrofit a legacy project with new QE practices; compare before/after metrics to win buy-in.
- Action: Set a 60-90 day PoC timeline. Example: “Transform CRM testing—cut defects by 50%, ship 30% faster.”
- Iterate and Expand
- New QCoE: After the PoC, refine based on feedback—tweak tools, processes, or team size. Then roll out to more projects.
- Transforming TCoE: Gradually phase out old TCoE habits; expand QE scope to new domains (e.g., mobile, cloud) as confidence grows.
- Action: Review quarterly—adjust KPIs, add 1-2 new capabilities (e.g., chaos engineering) per year.
Conclusion
In conclusion, the evolution from traditional Testing CoEs to modern Quality Engineering CoEs signifies a critical shift towards forward-looking, proactive and integrated quality assurance. By embracing advanced technologies like GenAI, cloud-based testing, and intelligent automation, organizations can transcend the limitations of reactive testing. The strategic implementation of a robust QCoE, as outlined through the ten actionable steps, enables businesses to not only enhance software quality but also drive innovation, improve customer satisfaction, and achieve a significant competitive edge. As the technological landscape continues to evolve, the ability to adapt and integrate these advanced QE practices will be essential for sustained success.