ContextQA Essentials: Building and Running AI-Powered Tests

Get the mental model right before running anything. This lesson sets mobile beside the web testing you already know: real devices instead of browsers, app builds instead of URLs, gestures instead of clicks. Just as useful is what stays the same, from plain-English authoring to variables, suites and plans.

From build file to passing test on real hardware. Upload an APK or IPA, choose the device and platform version, configure the run, and execute your first mobile test case. Then review the device logs, step screenshots and video, which let you diagnose failures without borrowing the device.

Two platforms, one result. Group mobile cases into a suite, then build a test plan targeting Android and iOS together so they run in parallel and report once. Also covers choosing device coverage sensibly, meaning the OS versions your users actually run, and scheduling cross-platform regression unattended.

The concept before the clicks, because knowing when to use an API test matters more than knowing how to build one. Learn what it verifies, why it is faster and steadier than the interface, and where the line sits: business rules at the API layer, layout and browser behaviour in the UI.

Build a request from nothing. Choose the method, enter the endpoint, add headers and a body, send it, then assert on the status code and specific response fields. Uses a real public login endpoint, and flags the mistake almost everyone makes: targeting the documentation page instead of the endpoint.

One test case, any number of servers. Lift the values that change, including base URLs, tokens and payload fields, into variables, then group them into environments for development, staging and production. Switching environment repoints the whole test without editing a step, and keeps secrets out of the test body.

Real API flows are sequences, not single calls. Capture a value from one response, such as a token or a newly created record ID, store it in a variable, and use it in the next request to build a realistic authenticate, create, read-back and verify chain. Includes how to debug a broken link.

The lesson that ties both halves of the course together. Call an API to create or fetch data, capture the response values, then use them to drive a browser login and validate the outcome end to end. It also removes slow UI setup: create records through the API in milliseconds.

“It failed” is not an answer anyone can act on. Root cause analysis identifies the step that broke and the likely reason: a real defect, bad data, timing, or a changed element. Impact analysis then shows which other tests and areas share it, telling you exactly what to re-run.

Any flow that creates something breaks the second time you run it, because the email or reference already exists. Generate names, emails, numbers and dates at execution time so every run submits fresh, valid, unique data, and control the format so it still passes the application's own validation.

The two features that keep a suite alive between releases. Break an element deliberately and watch ContextQA repair the locator mid-run once confidence clears the threshold. See the deliberate limit too: below it, the platform reports rather than guesses. Conditional logic then handles what self-healing cannot.

Generic AI produces generic tests. The Knowledge Base is where you give ContextQA what it cannot infer: your domain vocabulary, business rules, product workflows and team terminology. Add that context, then compare generated test cases before and after, the clearest demonstration of how much input shapes output.

Testing that ends at a red result is unfinished. Raise a Jira defect straight from a failed step with screenshots, logs and the root cause already attached. Coming the other way, turn an incoming bug report into a reproduction test so the fix is verified and cannot quietly regress.