The Future of automated testing

on YouTube ↗59m

" summary

Predicts testing shifts from expensive edge cases to cheap mass experiments by combining cloud farms, device probes and ML: runs on Amazon Device Farm and browser farms (BrowserStack/Sauce Labs) let mutation and naughty-strings suites scale; visual tools like Wraith visual diff and Applitools video diffs flag UI regressions; crowdsourced bursts (Mechanical Turk-style 5k sessions in CI) expose real failures across the projected ~20 billion devices (Gartner), replacing hair-trigger manual checks and surfacing surprising bugs like the Skype crash and Google’s “40 shades of blue” revenue impact.

AI-written summary · about

" chapters (8)

1. 0:00Start
2. 7:53Change the balance of expected vs unexpected
3. 15:10cloud device farms + browser farms + known testing heuristics
4. 19:17Assisting humans in making testing decisions
5. 36:10Dealing with things impossible to predict
6. 48:41Automated tests for behaviour changes in production
7. 50:29Big Brother, Big Data
8. 57:26Big-data threat/risk modelling

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