The Applicability of Machine Learning and Neural Networks in Test Automation
summary
The talk reviews real-world use of machine learning and neural networks in test automation, assessing market claims versus practical results. It covers ML-based approaches for categorizing automated test failures, compares available solutions, and discusses using neural networks to improve ML outcomes in testing.
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chapters (9)
- 0:00Introduction
- 3:16Overuse of ML in testing
- 7:11ML market analysis
- 10:04Defect classification limits
- 13:58Test case generation
- 17:12Neural network experiments
- 28:35Neural networks in classification
- 32:08Production test data
- 36:04Practical applicability
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