Development & technology · Test Automation & QA

AI training for Test Automation and QA

Quality assurance is under pressure: release cycles are getting shorter, applications more complex, and AI-assisted coding produces more code in less time – all of which needs testing. Test automation engineers and QA professionals therefore benefit particularly from AI assistants and agents. These tools derive test cases from user stories and acceptance criteria, write Playwright or Cypress tests with robust selectors, generate API tests from OpenAPI specifications, analyse flaky tests and build test data generators. At the same time the role is shifting: QA becomes the function that systematically safeguards AI-generated code too. This course teaches you to use AI for test design, automation and failure analysis and to adapt your test strategy to the new pace of development.

Who is this course for?

  • Test automation engineers and SDETs
  • QA engineers and test managers
  • Developers responsible for test suites
  • Business testers moving into automation

Typical challenges we address

  • Unstable end-to-end tests with timing issues and fragile selectors
  • High effort for test case creation, maintenance and test data
  • Gaps in coverage of APIs, edge cases and failure scenarios
  • Growing volumes of AI-generated code that need targeted safeguarding

Practical scenarios from your field

Deriving test cases from requirements

The assistant turns user stories and acceptance criteria into structured test cases with equivalence classes, boundary values and negative scenarios, optionally as Gherkin scenarios for behaviour-driven development.

Writing Playwright and Cypress tests

The AI writes end-to-end tests with role-based selectors, page objects or fixtures and proper waiting instead of fixed delays, and runs them iteratively until they are stable.

Analysing flaky tests

The agent examines test history, traces, screenshots and logs, identifies causes such as race conditions, test interdependencies or unstable test data, and proposes lasting fixes.

API tests from specifications

OpenAPI descriptions are turned into API tests for status codes, schemas, authentication and error cases, plus contract tests that surface interface changes between teams early.

Generating test data

The AI builds generators for realistic synthetic test data, factories and seed scripts that deliberately cover edge cases without relying on real personal data. Privacy issues in test environments are avoided.

Coverage and mutation testing

Coverage reports and mutation testing results are analysed, untested paths identified and missing tests added in a targeted way to increase the real value of the suite.

Possible hands-on projects

Flaky test report

A script that merges JUnit XML results from multiple CI runs, detects unstable tests and produces a prioritised list with failure patterns.

Test data generator

A CLI that creates realistic synthetic records, including edge cases, from a database schema or JSON schema. Data volume and distribution are fully configurable.

E2E suite for a demo app

A Playwright test suite with page objects, visual comparisons and CI integration, built by an AI agent for an existing web application.

All 10 courses for Test Automation and QA

Vibe & agentic coding (10)

Data protection & compliance

Test data should be synthetic or effectively anonymised, as production data containing personal information must not be used unprotected in test environments or prompts (GDPR). Credentials for test systems belong in secret stores rather than test code, and in regulated sectors test results must be traceable and documented, for example as evidence under NIS2 or the Cyber Resilience Act.

Prerequisites

Experience in software testing or test automation and a working knowledge of at least one programming language such as JavaScript, TypeScript, Python or Java.

Frequently asked questions

Do I need programming skills for this course?

A working knowledge of a programming language helps, since you should be able to read, understand and adapt generated tests. Business testers with little coding experience still benefit greatly, because the AI explains code and supports the move into automation. The exercises are designed to accommodate different levels of prior knowledge.

Will AI replace manual and exploratory QA?

No. AI automates recurring tasks such as test case creation, scripting and failure analysis, freeing up time for exploratory testing, risk assessment and acceptance. With AI-generated code in particular, the critical judgement of experienced QA professionals becomes more important, not less.

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Total €949 plus VAT