AI in Automation Testing Tools, Practice & Scale

A practical guide to AI across automated testing - Playwright, Selenium, Cypress, real suites, and what breaks at scale

What you'll learn

⚡ Choose between Playwright, Selenium and Cypress for a given project, and justify the choice with real trade-offs
⚡ Write resilient locators, and explain why most generated locators break within weeks
⚡ Generate tests from requirements, recordings and existing code - then cut the ones not worth keeping
⚡ Write assertions that actually catch regressions, rather than the weak ones generation produces by default
⚡ Tell the six causes of test flakiness apart, and fix each without adding another sleep
⚡ Run a quarantine that does not become a graveyard of permanently ignored tests
⚡ Maintain a suite of hundreds of tests as the application changes underneath it
⚡ Delete tests safely, and measure coverage in a way that means something
⚡ Cut pipeline time with parallelism, sharding and test selection, and know what each costs
⚡ Debug a CI failure you cannot reproduce locally, using traces, artefacts and logs
⚡ Judge where AI-native tools, self-healing locators and visual diffing belong next to a code suite
⚡ Spot a test that passes for the wrong reason, which is more dangerous than one that fails

Requirements

❗ You should be comfortable reading and writing code in at least one language - jаvascript, TypeScript, Python, Java or C# are all fine
❗ Some exposure to automated testing helps, but you do not need to be an expert in any of the three tools
❗ A machine you can install Node and a browser driver on, and ideally a real project with an existing test suite

Description

This course contains the use of artificial intelligence.
AI is used to reframe the words, fixing spelling mistakes and grammatical mistakes and audio conversion.
A generated test suite is easy. A generated test suite that still runs in six months is not.That gap is what this course is about. Most material on AI and testing stops at the demonstration - a tool writes a working test in ten seconds, everybody is impressed, and nobody shows you the same suite half a year later when the selectors have drifted, forty tests are quarantined, the pipeline takes fifty minutes, and the team has quietly stopped believing a red build.
This course covers the whole life of an automated suite. You will work across the three tools most teams actually use - Playwright, Selenium and Cypress - with a shorter section on AI-native tooling, self-healing locators and visual diffing, so you can judge where those belong alongside a code suite rather than instead of one. Then the parts that decide whether automation survives: generating tests that are worth keeping rather than deleting, telling the six causes of flakiness apart instead of adding another wait, maintaining a suite as the application changes underneath it, deleting tests without losing coverage, and getting a pipeline fast enough that people wait for it.
It is honest about where AI does not help. Generated assertions are frequently the weakest part of a generated test. A self-healing locator that quietly repairs itself can hide a genuine regression. A test that passes for the wrong reason is more dangerous than one that fails, and there is a full lecture on finding those. You will learn to spot each, because a suite nobody trusts is worse than no suite at all.This is a hands-on course for QA engineers, SDETs, automation testers and developers who own their own tests. You should be comfortable reading and writing code in at least one language - jаvascript, TypeScript, Python, Java or C# are all fine, and examples are given in a way you can follow whichever you use. It does not assume you have used AI tooling before.
Every section ends with a hands-on exercise against a real suite, so you finish with tests you generated and cut, a measured flake rate, a suite health audit, and a pipeline you have actually made faster.This course is not affiliated with, endorsed by, or sponsored by any tool vendor. Tooling in this area changes quickly, so the course teaches the judgement about what to use when - which outlasts any particular release.

Who this course is for

⭐ QA engineers and automation testers who maintain a suite that is getting harder to trust
⭐ SDETs and test architects choosing tooling for a new project or a migration
⭐ Developers who own their own tests and want them to stop being the slowest part of the pipeline
⭐ Test leads dealing with a flaky suite, a slow pipeline, or a team that has stopped believing red builds
⭐ Anyone who has generated tests with AI and watched most of them get deleted a month later

Homepage

https://www.udemy.com/course/ai-in-automation-testing-tools-practice-scale

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