QA & Test Automation

AI-SAI-Generated Summary H-RHuman Reviewed H-EHuman Edited

Transfer Online did not yet have a formal QA process, so I created and presented the initial strategy, developed the test plan and cases, led manual and exploratory testing, and then built a reusable Playwright framework for regression and integration coverage. I organized repeated application behavior behind larger workflow helpers, which made the suites easier to read and reduced duplicated test logic. See the public Playwright Testing Documentation.

Note

Implementation timeline I designed and implemented the underlying QA strategy, Playwright framework, reusable helper patterns, configuration model, and initial regression automation before generative AI was introduced. Later additions generated and repaired tests according to the existing framework and quality standards.

View the complete implementation timeline.

Later, I added rules and skills that converted Playwright codegen output into readable tests following those established helpers and standards. A related workflow used validated automation functions to produce documentation. The system could also start from a URL, capture the interaction flow, and develop the test and documentation together, with execution-based checks and human review before acceptance.

The framework remained useful for exploratory testing because a tester could adjust a workflow manually or through the agent, rerun it quickly, and investigate failures without changing the underlying quality strategy.

Playwright architecture and helper walkthroughs

The homepage’s QA & Playwright Test Automation section brings the case study and implementation walkthroughs together. These pages explain the framework through source examples:

• Suite evolution — The progression from recorded browser actions to reusable functions, shared setup, and configuration.

• Functions explained — The guide to email templates, primary and mailing addresses, named screenshots, and selector diagnostics, with links to each full walkthrough.

• Proof of Address walkthrough — A complete test showing how imports, lifecycle hooks, helper calls, and screenshots work together.

• Agent workflow — How stories, helper-based tests, execution feedback, and repairs built on the existing framework.

Results, pipeline, and related experiments

The homepage’s Testing Results & Engineering Experiments section groups the supporting evidence and delivery workflow alongside a separate experiment in model-generated documentation:

• Testing and Results — Reproducible exploratory inputs, stability analysis, and reported output metrics.

• Codegen-to-Documentation Pipeline — The workflow from recorded interactions and configuration to tests, execution checks, and published documentation.

• LLM Fine-Tuning Experiment — A related experiment in generating C# XML documentation comments with a small model, including training details and example outputs.

• Parallel Scaling: Worker Count vs Pass Rate — Worker-count comparisons showing pass rates, execution times, and the underlying run data.