Video Summary and Transcription
Hello, everyone. Today we are going to explore AI-powered end-to-end testing. Unlike unit tests, UI testing has a huge layer of obstructions between the source code and the rendered UI. The source code includes HTML, CSS, and TypeScript, which are transpiled into JavaScript and bundled with tools like Webpack. AI can generate tests effectively for standard websites or blogs, but it may struggle with niche applications behind strict authorization or on-premise tools. AI-powered end-to-end testing for complex scenarios requires our guidance. We use meaningful data test IDs and follow the page objects model pattern. Additionally, we rely on useful tools like the end-to-end test helper in-browser extension and the continue IDE extension. Now, let's proceed to the demo, where we will create tests for the Pokemon application, including the ability to filter by name or type. We will navigate to the Pokemon details page and use our extension to manage settings and prompts. Additionally, we will create the details page object together and generate the test file. The Pokemon details page has 105 elements. We can view the elements for debugging purposes, including page object name, Pokemon details page, and system message. We will copy the page object and save it to a file. We need to make it exportable. Then, we will use the extension to create an end-to-end test and pass the context. I will use all the open files, including the page objects and the test case itself. I will send them to EI along with the predefined prompt. There is a system message and additional information we need to be aware of. The test runs successfully, and that concludes this part.
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