Rise of the Machines: Can Artificial Intelligence Terminate Manual Testing?
summary
AI and ML already augment testing and target automated oracles, test generation, system modeling, and defect discovery with concrete tools and datasets. A crawler called Dora builds UI models, learns from user stories and existing test cases using NLP/LSTM, and combines neural nets, Bayesian nets, decision trees and reinforcement learning to generate readable tests. The talk cites large datasets (eg 30 million moves analogy), drives apps via Selenium/Robot Framework, runs tests in production with safe‑for‑patient validation and replicas, but the oracle problem remains.
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chapters (40)
- 0:00Introduction
- 0:54Tariq King
- 2:23What is testing
- 5:12What is software
- 5:52Artificial Intelligence
- 9:04Chess
- 13:22Goal
- 22:40Deep Learning
- 29:46Coverage
- 31:03Software Complexity
- 31:40Artificial Intelligence and Testing
- 32:13The bots are here
- 34:28How does it work
- 36:43Dora
- 38:05Dora learns
- 39:23How it works
- 39:54Benefits of testing
- 41:10Limitations
- 41:23Whitebox Testing
- 41:50Trust in Technology
- 44:06Dynamic Systems Initiatives
- 45:46Testing is part of the system
- 46:06In production
- 48:17Manual Testing
- 51:50The Beginning
- 52:07AI Machine Learning
- 53:18Its Real Life
- 53:37The Challenge
- 54:10One grand vision
- 54:26No human intervention
- 55:02Three Areas of Intersection
- 55:36Our Vision
- 55:49Community
- 56:03Testing the Org
- 56:26Communication Collaboration
- 56:59The End
- 57:13The Greatest Conference
- 57:26Sharing and Diversity
- 58:26Increasing Knowledge
- 58:58Question Time
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