QA Lab
A living test environment for automation framework design, tool evaluation, AI-driven development, and DevOps experimentation.
About the Lab
The QA Lab is my personal sandbox for hands-on experimentation with automation frameworks, testing tools, CI/CD pipelines, and AI-assisted development practices. Three core pillars drive the work:
Frameworks & Tool Evaluation
Architect and prototype test automation frameworks across different stacks — Playwright, Selenium, Cypress, and AI self-healing tools like Testim and Mabl. Benchmark tools against real UI elements to produce evidence-based selection recommendations grounded in hands-on experience, not vendor docs.
AI-Driven Development
Explore and refine AI-assisted workflows for test generation, code review, and coverage analysis. Polish prompt engineering techniques for QA use cases using GitHub Copilot, Claude, and emerging AI tooling. Measure actual output quality — AI earns its place the same way any QA instrument does.
DevOps & CI/CD
Build and iterate on CI/CD pipeline quality integration patterns — quality gates, nightly regression pipelines, SAST/DAST toolchain integration, and observability hooks. Practice DevOps disciplines end-to-end using Azure DevOps, Jenkins, Docker, Snyk, and SonarQube.
Lab as a Product
The QA Lab is managed as a real software product with PMI-compliant project documentation, a living roadmap, and a structured backlog. This is intentional — it serves as a vehicle to practice and polish AI-driven development, DevOps disciplines, and iterative delivery in a low-risk environment before advocating for the same practices in enterprise settings.
Project Governance
- Project Charter & Scope Definition
- Requirements Traceability Matrix
- Risk Register & Mitigation Plans
- Change Log & Decision Records
Roadmap & Backlog
- Feature Roadmap with quarterly themes
- Prioritized improvement backlog
- Definition of Ready / Done for lab tasks
- Sprint-style iteration cycles
AI-Driven Development
- AI-generated test cases & scaffolding
- Claude & Copilot-assisted code review
- Prompt libraries for QA use cases
- AI coverage gap analysis experiments
DevOps Practices
- CI pipeline for automated lab test runs
- Docker-based environment isolation
- SAST/DAST tool chain experiments (Snyk, SonarQube)
- DORA metrics tracking for the lab itself
What’s in the Lab
Lab As Is
The live UI testing surface — a comprehensive set of interactive elements (buttons, forms, tables, modals, sliders) designed as a real automation target. Use it to run framework experiments directly.
Documentation →
PMI-compliant project documentation: charter, test strategies, architecture decisions, traceability matrices, and framework guides — organized and versioned as a real product would be.
Blog →
Write-ups on tools evaluated, experiments run, and lessons learned in the lab. Covers automation framework choices, AI tooling discoveries, and DevOps integration patterns.
Cross-Stack Series
Five production-grade automation frameworks targeting this QA Lab — same UI surface, different stacks. Each is a standalone portfolio artefact with live CI/CD, published test reports, and full documentation. Built to enable evidence-based comparison, not vendor benchmarks.
Playwright + TypeScript + Allure
Reference stack. Typed fixtures with test.extend(), @step decorators, soft assertions, fullyParallel CI. Covers all 14 QA Lab sections including IFrames via frameLocator(). Runs cross-browser: Chromium, Firefox, WebKit.
Pytest + Python + Allure
Idiomatic Python stack. yield fixtures, conftest hierarchy, @pytest.mark.parametrize data-driven tests, pytest-xdist parallel execution, mypy strict typing, ruff linting. Allure report published to GitHub Pages.
Selenium + Java + TestNG
Enterprise Java stack. Page Factory, ThreadLocal WebDriver parallel-safe, @DataProvider, ExtentReports with ITestListener, Checkstyle enforcement, Maven Surefire CI. Demonstrates the patterns expected in fintech and legacy enterprise environments.
Cypress + JavaScript + Mochawesome
Browser-native stack. cy.intercept() network stubbing, cy.session() auth caching, time-travel debugging, @smoke tag filtering via @cypress/grep. IFrame limitation documented as an engineering trade-off. Mochawesome report on GitHub Pages.
Playwright + C# + NUnit
Strongly-typed .NET stack. Identical Playwright engine as Stack 1, different language — a clean TS vs C# comparison. NUnit [TestCase] parametrization, async/await throughout, Playwright HTML Reporter on GitHub Pages.