Training course · Cursor · Test Automation & QA

Cursor for Test Automation and QA

Work productively with the AI code editor Cursor: agent mode, rules, codebase context, Tab completion, background agents and review.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

This course connects Cursor with the everyday processes of Test Automation & QA. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Cursor is an AI code editor built on VS Code that weaves language models deeply into everyday development. Alongside context-aware Tab completion, it offers an agent mode that searches files on its own, plans changes across the whole project, runs terminal commands and fixes errors iteratively. In this two-day course you will learn to use Cursor with purpose: choosing the right interaction mode, steering context deliberately and combining AI assistance with your existing workflow of version control, automated testing and peer code review.

A key focus is making Cursor work for teams. Project rules let you capture architectural decisions, naming conventions and testing strategies so that the agent produces consistent results. You will learn to make good use of codebase indexing, targeted references to files, documentation and symbols, and MCP integrations. You will also delegate tasks to background agents, review their output as pull requests and set up a review process that takes quality, security and data protection seriously across the organisation.

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.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

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

What you will be able to do

  • You choose the right Cursor mode for each task and use it efficiently.
  • You steer codebase context precisely and get more accurate suggestions as a result.
  • You write project rules that reliably enforce your team’s conventions.
  • You use agent mode and background agents for features and refactorings with verifiable results.
  • Derive test cases systematically from requirements with AI and automate them in Playwright, Cypress or API tests.
  • Analyse flaky tests and coverage gaps with AI support and fix them for good.
  • Adapt your test strategy to AI-assisted development and safeguard generated code in a targeted way.

Course outline

Day 1

Module 1 Setting up Cursor and understanding its modes
  • Installation and migration of VS Code settings and extensions
  • Differences between Tab completion, inline edits, Ask mode and Agent mode
  • Choosing a model per task: balancing speed, depth and cost
  • Privacy mode and organisation-level settings
Module 2 Steering codebase context
  • How codebase indexing works and where its limits lie
  • Referencing files, folders, symbols, documentation and web sources precisely
  • Using ignore files to exclude sensitive or irrelevant content
  • Avoiding overlong chats and restarting tasks with fresh context
Module 3 Rules for consistent team results
  • Creating and versioning project rules and scoping them by file pattern
  • Expressing architecture, style and testing conventions as effective rules
  • Separating user rules from team rules
  • Refining rules iteratively based on the agent’s typical mistakes
Module 4 Agent mode in practice
  • Building features step by step with plan, implementation and tests
  • Running multi-file refactorings safely with checkpoints
  • Approving terminal commands deliberately and feeding error output back
  • Connecting MCP servers for issue trackers, databases or browser testing

Day 2

Module 5 Background agents, review and governance
  • Delegating tasks to background agents and running them in parallel
  • Reviewing results as branches or pull requests and steering follow-ups
  • Using AI-assisted code review alongside human review
  • Addressing security risks, secrets and licensing in enterprise use
Module 6 Practice lab: Test Automation & QA
  • Deriving test cases from requirements
  • Writing Playwright and Cypress tests
  • Analysing flaky tests
  • API tests from specifications
  • Generating test data
  • Coverage and mutation testing
Module 7 Data protection, quality & transfer to daily work
  • 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.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

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.

Prerequisites

You program regularly in at least one language and are familiar with Git and an IDE such as VS Code. Experience in software testing or test automation and a working knowledge of at least one programming language such as JavaScript, TypeScript, Python or Java.

Technical requirements

Your own laptop with Cursor, Git and your usual development environment installed; a Cursor account with access to agent features is recommended.

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.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Wed, 9 December 2026 – Thu, 10 December 2026 Stuttgart Request
  • Mon, 11 January 2027 – Tue, 12 January 2027 Online Request
  • Wed, 20 January 2027 – Thu, 21 January 2027 Berlin Request
  • Wed, 27 January 2027 – Thu, 28 January 2027 Online Request
  • Mon, 8 February 2027 – Tue, 9 February 2027 Munich Request

Frequently asked questions

Do I need prior VS Code experience to use Cursor?

It helps, but it is not essential. Cursor is built on VS Code, so navigation, shortcuts and extensions feel familiar and existing settings can be imported. If you currently work with JetBrains IDEs or other editors, a short introduction during the course gets you up to speed quickly so you can concentrate on the AI features.

How do I prevent confidential code from reaching third parties?

Cursor offers a privacy mode and organisation settings that restrict how code is stored by the provider. You can also use ignore files to exclude sensitive areas from indexing and context. In the course we discuss how to align these options with internal policies, data protection requirements and sound handling of secrets.

What do rules add compared with well-written prompts?

A prompt applies to one request only, whereas rules persist and apply to the whole team. They capture architectural decisions, preferred libraries, testing strategies and style guidelines, so nobody has to repeat that information and the agent produces code that fits your codebase. Rules are versioned with the project and evolve together with it.

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.

How much does “Cursor for Test Automation and QA” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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