Practical notes on AI, QA Automation, software testing, performance testing, and modern QA workflows.
Flaky tests quietly destroy trust in a test suite. A concrete playbook for finding the flakes, diagnosing the real cause, and stopping them from coming back.
How to actually read load-test results — why the average response time hides your worst problems, and how percentiles, throughput, and error rate tell the real story.
Reusable prompt patterns that make an LLM produce useful, structured test cases instead of vague suggestions — with the guardrails that keep it honest.
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 repeatable method for locating the real bottleneck in a slow system — measure before you guess, follow the resource, and confirm the fix instead of hoping.
A phased roadmap for growing test automation from a fragile pilot into a maintained, CI-integrated suite that the whole team trusts.
A practical pre-release checklist for mobile apps — the device, network, lifecycle, and store-readiness checks that catch the bugs desktop testing never will.
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.
A practical Definition of Done that bakes quality into every story — so "done" means shippable, not just "the code compiles on my machine."
Practical notes on using Robot Framework with the RequestsLibrary for readable, maintainable API tests that non-developers can still follow.
The four performance test types answer four different questions. A practical guide to when to run each one, and what a passing result actually tells you.
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 good bug report gets fixed fast; a vague one bounces back and forth for days. The anatomy of a report that a developer can act on immediately.
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.
No notes in this category yet — check back soon.
