Practical notes on AI, QA Automation, software testing, performance testing, and modern QA workflows.
Using AI to speed up defect analysis — clustering duplicate reports, suggesting likely root-cause areas, and drafting reproduction steps — without outsourcing judgment.
A repeatable workflow for turning a requirement or user story into positive, negative, and edge-case test cases with an LLM — without letting the model invent coverage.
A phased roadmap for growing test automation from a fragile pilot into a maintained, CI-integrated suite that the whole team trusts.
How I turn an OpenAPI/Swagger spec into a prioritized set of API test cases and a runnable Postman collection, without hand-writing every request.
You can never test everything. Risk-based testing is a practical method for spending your limited testing time where a failure would hurt most.
The structural decisions — page objects, fixtures, config layering, and reporting — that keep a test framework maintainable as it grows past a few hundred tests.
Practical notes on using Robot Framework with the RequestsLibrary for readable, maintainable API tests that non-developers can still follow.
The test pyramid is a useful heuristic that is easy to misread. A practical look at what each layer is for, and how to keep the shape from inverting.
How to turn scattered test notes, bug patterns, and tribal knowledge into a searchable QA knowledge base — and where AI genuinely helps versus where it gets in the way.
A practical comparison of JMeter and k6 for load testing — not a winner-takes-all verdict, but a decision guide based on team skills, protocols, and CI needs.
A practical pre-release checklist to confirm a performance test will produce trustworthy results — environment parity, data, SLAs, and monitoring — before you generate load.
A simple, repeatable workflow for triaging incoming bugs so nothing important gets lost and nothing trivial blocks a release.
A practical checklist for verifying that an Android app's root and tamper detection actually works — and holds up against common bypass techniques.
When to test on emulators, when you need real devices, and how to build a device strategy that balances coverage against cost.
How to structure Postman collections and environments so an API regression suite stays fast, readable, and runnable in CI with Newman.
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