Future of AI ML in Software Testing
summary
Claims that AI makes test suites resilient by automatically repairing broken locators, citing a survey that 75% of test automation scripts fail from locator changes; demo uses an open‑source Selenium adapter called helenium that records working locators, calls a backend ML model to propose a healed locator, and upgrades MP_password_1 to password at runtime when allowed, eliminating repeated manual edits. Notes it reduces maintenance time but needs compute, high‑quality data, tooling choices and org privacy controls.
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chapters (13)
- 0:00Introduction
- 1:19AI Machine Learning and Deep Learning
- 2:44Three Generation of AI
- 5:26Challenges
- 7:40Solution
- 10:12Helix
- 11:39Helix Example
- 15:49Benefits
- 17:31Challenge
- 19:14Current Applications
- 23:43Will AI replace Software Engineers
- 25:06QA
- 33:08Automation from scratch
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