Future of Software Testing: Machine Learning Assistance
summary
Claims that machine learning helps every testing stage and shows concrete prototypes: an API-driven test generator learns input–output patterns and emits negative cases; a visual, self-healing UI automation reduces brittle selectors; an execution monitor flags runtime outliers; and a Jira bug-severity classifier trained on issue text initially fails on six-way labels due to bias but, when collapsed to binary, reaches 82% accuracy. Mentions feature extraction, supervised classifiers, and the practical constraint that data balance dictates whether models actually work.
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chapters (17)
- 0:00Start
- 3:11CHALLENGES
- 5:05Can they help?
- 7:52Already being used widely
- 9:30Learning: Meaning
- 13:19Background
- 13:36Algorithms
- 14:30Software Testing Stages
- 15:28Definition / Case Generation
- 18:05Implementation / Code Generation
- 19:50Implementation / Execution
- 20:32Maintenance / Refactoring
- 20:53Maintenance / Self Healing
- 21:29Maintenance / Bug Management
- 21:56Bug Severity Estimation
- 22:40Who is right?
- 23:36Summary
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