AI for QA & Software Testing

QA engineers and testers learn to accelerate test design, generation, and analysis with AI. The course covers test-case generation, automation scripts, bug triage, coverage, and review. Participants finish able to expand coverage and catch issues sooner.

What this course covers

The course runs across 8 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

Introduction to AI-Assisted QA and Test Design — Understanding how LLMs and AI assistants accelerate test planning, draft test cases, and identify edge cases from requirements.
Hands-on Test Case Generation with AI — Using AI tools to generate comprehensive functional, negative, and boundary value test cases based on user stories.
Generating Test Automation Scripts — Leveraging AI to write, refactor, and translate test automation scripts across different frameworks like Selenium, Cypress, and Playwright.
AI-Driven Test Data Generation — Creating realistic mock data, databases, and API payloads dynamically using AI prompts and structured schemas.
Automated Bug Triage and Log Analysis — Using AI to analyze stack traces, parse application logs, isolate root causes, and automatically draft detailed bug reports.
Optimizing Test Coverage and Gap Analysis — Using AI to analyze existing test suites against codebase changes to identify coverage gaps, redundant tests, and high-risk areas.
AI Code Review for Test Automation — Applying AI to review test automation code for best practices, flakiness, maintainability, and execution efficiency.
Integrating AI into the CI/CD Testing Pipeline — Best practices for embedding AI-driven testing tools into continuous integration workflows and managing AI test maintenance.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick. This course maps to Generative AI Concepts, Data Analysis, AI Ethics, Content Strategy, Critical Thinking.

Generative AI Concepts
Data Analysis
AI Ethics
Content Strategy
Critical Thinking

Who it is for

QA engineers, testers. The material is pitched at intermediate level, and takes roughly 120 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Function: QA & Testing track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including guided lab, code lab exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a code assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Frequently asked questions

How long does this course take?

It runs roughly 120 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code FUN-QA.