Welcome to the Automation Lab! Here you can find practice pages and demo apps designed for test automation. Use these to practice locating, interacting, and automating a variety of frontend elements and APIs. More AI-powered test apps coming soon!
Practice pages for locators, forms, and table automation.
Type CSS or XPath, highlight matches, and practice deep search across shadow DOM and iframes.
Practice with text fields, checkboxes, radios, dropdowns, and richer form interactions in a realistic UI.
Automate sorting, filtering, and interacting with tabular data with modern grid patterns.
API, UI, and distributed-system testing tracks.
Master REST API testing principles, request strategies, response validation, and building comprehensive API test automation.
Learn core web UI testing concepts including element interaction, validation strategies, synchronization, assertions, and reliable end-to-end quality checks for modern interfaces.
Learn microservices testing essentials including service contract testing, integration strategies, resilience validation, and end-to-end quality checks across distributed systems.
Learn a practical QA automation platform approach for planning, designing, and scaling AI-era testing workflows with reusable strategy patterns.
Explore a Claude-native QA automation system with MCP-driven workflows, agent orchestration, and scalable quality engineering patterns.
Premium browser automation and AI-driven testing tracks.
Decision Guide · AI Integration · Selenium · Playwright · Cypress · Puppeteer · WebdriverIO · UFT & more
Paid PreviewMaster LLMs, prompt engineering, AI-assisted test generation, self-healing locators, and building an AI-first QA strategy with flashcards, MCQs, and code examples.
Learn AI agent concepts for QA teams, including agent workflows, test planning prompts, automation use cases, and practical examples for modern testing pipelines.
Validation, risk controls, and trustworthy AI quality workflows.
Explore AI assurance principles for quality teams, including model validation, risk controls, responsible AI checks, and practical QA workflows for trustworthy AI systems.
Deep dive into observability, tool call testing, benchmarks, regression testing, security and production monitoring for multi-agent AI pipelines.
