AI-Driven Test Case Generation
How to use LLMs to generate test cases from user stories, requirements, and production logs, plus patterns for reviewing and maintaining AI-generated suites.
Hi! I'm Oleg Neskoromnyi — QA engineer exploring AI in testing.
Learn how to use AI for test generation, execution, and analysis with real-world experiments, honest results, and lessons learned from software QA teams.
Practical, AI-focused guides for every layer of your QA workflow.
How to use LLMs to generate test cases from user stories, requirements, and production logs, plus patterns for reviewing and maintaining AI-generated suites.
Architecture patterns for integrating AI into your existing UI and API automation, with real examples of what actually improves maintainability and what doesn't.
Prompt templates for test case design, bug reproduction, risk analysis, and exploratory testing that work in real CI/CD pipelines, not just demos.
Using AI to shift from reactive bug-catching to proactive quality strategies, with reporting and metrics leaders actually care about.
Deep-dive guides based on real experiments — not vendor decks.
Early impressions of Claude Code's /btw side questions and /voice input — from someone who already pays for Superwhisper.
My army of AI agents was supposed to test an app, generate automation, and run it live. Nothing worked. That turned out to be the best thing that could have happened.
After 15 years in QA leadership, AI tools let me start building the solutions I'd always imagined. This is the story of how a tester became a builder.
Learn a practical 5-layer testing strategy for AI-generated code, built from real QA experience and backed by research from Anthropic and OpenAI.
Two decades of leading QA teams, building automation frameworks from scratch, and shipping quality software under real constraints. Now I experiment with AI-powered testing agents and share everything I learn on QaHub.AI - the wins, the failures, and the practical workflows that actually ship.
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