AI-assisted SQA workflow

Practical AI use in testing and automation — with reviews, validations, and ownership staying on the QA side.

I use modern AI assistants the same way I use test tools: to shorten feedback loops, surface edge cases, and reduce repetitive work — not to replace judgment, evidence, or accountability.

AI for SQA work

  • Cursor — AI-assisted test case drafting, refactors, and quick debugging with human review.
  • Claude — Analyzing test strategy, edge cases, and regression scope before changes go live.
  • OpenAI Codex — Assisted automation and tooling work; every change still passes review and validation.
  • GitHub Copilot — Speeding up repetitive SQA code, assertions, and small utility tasks.
  • ChatGPT & Gemini — Research, bug-hypothesis checks, and test idea generation — not a substitute for validation.

Testing & delivery tooling

  • Postman — API collections, environments, and manual verification of endpoints before merge.
  • JMeter — Load and performance checks to spot slow endpoints and risky response patterns.
  • Selenium / Cypress / Appium — Web and mobile automation coverage for smoke, regression, and sanity testing.
  • Jenkins / Git & GitHub — CI execution, branching, pull requests, and review gates for reliable delivery.

Ship-ready quality still means tests, evidence, and team standards — assistants reduce friction; they do not replace accountability.