Karate Framework UI & Performance Testing

Learn how to perform performance testing with Karate Gatling, using Karate test code as the load model, configured via Maven plugins, and validate response times and codes under load.

Configure Karate Gatling in a Maven project by selecting an editor (prefer Visual Studio Code), adding the Gatling and Scala plugins and dependencies in pom.xml, and using Copilot for assistance.

Configure karate with gatling by adding a demo performance feature and updating the pom.xml. Create performance, config, and data folders for the simulation, then run the tests.

demonstrate using the karate protocol pauseFor() to introduce delays for get requests in milliseconds, showing how 10,000 ms delays affect multiple scenarios and request-per-second timing.

Learn how to use the Karate runner, including env, config dir, and config.js to load environment-specific base URLs. See an example with the dev environment and system properties for configuration.

Learn to use the karate data feature to tag and run specific scenarios with a tag selector, and review reports to verify targeted execution.

Explore how to access Gatling session variables in Karate feature files using copilot, printing user id, token, and role, and learn practical session data retrieval.

Retrieve Gatling session data in a karate program using feeders, load data from a csv via a data feed, and print email id and username in a scenario.

Learn how to use a csv feeder in Gatling to feed username and email into a user creation flow and substitute data via a json test file.

Learn to configure a json feeder in Gatling, read data from a json file in a simulation, format data as an array, and print fields like email and password.

Explore open versus closed workload models, controlling arrival rate with an open model, and injecting at once users with pauses, illustrated by two then four users and a requests-per-second graph.

Explore what artificial intelligence means, how machines learn from large data, and how AI can perform language tasks, image recognition, and even automate test cases in software.

Explore human-in-the-loop AI in automation and testing, balancing AI speed with human review to ensure accurate and reliable outcomes.

Generative AI creates new content, unlike traditional AI that analyzes existing data; it can generate code, documents, images, audio, and video from inputs.

Explore how AI memory stores, retains, and retrieves past interactions, comparing short term memory within a session to long term memory across sessions, using real-world chatbot examples.

Explore the model context protocol (MCP) as a universal adapter that lets AI models call tools and APIs through a standardized, reusable interface, detailing tool descriptions, inputs, and outputs.

Fine tuning updates the existing knowledge base of an AI agent to tailor interactions with customers, staff, and executives, unlike retrieval augmented generation which uses external knowledge.

Learn how to configure a context for llms to act as a tester, generate manual and automation test cases, and align with framework rules, acceptance criteria, and user interface mockups.

Create a manual context file and use ChatGPT with an attached document to define a demo.com login flow, including positive and negative tests. Explore prompts, acceptance criteria, and prototype considerations.

Explore how OpenAI drives AI development with GPT models and DALL-E, revealing behind-the-scenes roles of ChatGPT and Copilot, and OpenAI's mission to benefit humanity.

Explore what an AI model is—a data-trained algorithm that recognizes patterns and generates outputs, with examples like GPT and dalle E, and distinguish AI models from LLMs as specialized tools.

Discover how the n8n workflow platform uses ai to build customized agents without coding. Explore automating daily tasks, emails, and data handling through trial accounts and sample workflows.

Sign up on the OpenAI platform and use quick start to create a new secret API key. Copy the key and connect your AI agents to OpenAI.

Create a practical n8n workflow that connects Gmail and Google Drive, reads a test data sheet, uses an AI agent with OpenAI GPT-4.1 mini to filter data, and sends emails.

Build an n8n workflow that reads a sheet, checks for an email value, and conditionally sends emails using an AI agent, with the model dynamically selecting the recipient.

Create a workflow in n8n part 3 that reads sheet two in Google Sheets, checks if status is S, and sends background verification emails to multiple candidates.

Explore building an n8n workflow to create Jira issues from spreadsheet data, including configuring Zephyr, authenticating with email, API token, and domain, and troubleshooting project connections.

Use an n8n workflow to automate creating Jira bugs from sheet data, validating credentials and URLs, and generating defects (Luke 1 and Luke 2) when status is new.

Create a chrome extension for record and playback that generates Playwright TypeScript or Selenium Java scripts from browser actions, with a user interface, record and playback controls, and download options.

Create the OTP shield mobile app that monitors text messages, phone calls, and WhatsApp for OTP requests, warns users, and blocks apk files.

Generate karate UI test cases using Copilot in Eclipse, applying given-when-then with precise locators and unique test names. Analyze app pages and prompts to craft robust login flows.

Generate Karate UI test cases by analyzing the application with Cursor AI, capture locators, and create a feature file that clicks the login button on the home page.

Create a manual test case generator agent by defining a markdown-based sub-agent, outlining rules, and detailing how to fetch Azure stories, generate test cases, save drafts, and optionally upload.

Learn to generate comprehensive manual test cases with end-to-end scenarios and priority tagging, then use the Libby manual agent UI to create, edit, and save them as story-id.md.