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  • Executive Introduction
  • 1: Get Started 1: Get Started
    • 1: Get Started
    • 1: Get Started - an introduction
    • Quality Engineering and recent trends
    • Getting started with AI
    • Demystifying Machine Learning for QE
    • Demystifying Deep Learning for QE
  • 2: Design 2: Design
    • 2: Design
    • 2: Design - an introduction
    • Moving to an AI-driven digital twin
    • AI as co-creator of test design
    • The power of process intelligence
    • Dynamic optimization of tests
    • Another view on test case optimization
  • 3.1: Inform & Measure 3.1: Inform & Measure
    • 3.1: Inform & Measure
    • 3.1: Inform & Measure - an introduction
    • Smart quality gates
    • From good metrics to smart analytics
    • Code Quality Analytics
    • Code analytics extended to impact based testing
    • Predicting Change Risk with Reliability
  • 3.2: Inform & Interpret 3.2: Inform & Interpret
    • 3.2: Inform & Interpret
    • 3.2: Inform & Interpret - an introduction
    • NLP & NLU Fundamentals
    • NLP For duplicate and orphans assets
    • Bringing NLG into Software Development
    • NLP for downstream recommendations
    • NLP and sentiment analysis
  • 4.1: Automate & See 4.1: Automate & See
    • 4.1: Automate & See
    • 4.1: Automate & See - an introduction
    • Making in-sprint UI Test Automation a reality
    • Shorten release cycles with Visual AI
    • Lower test automation barriers with computer vision
    • Testing above and below the application surface
    • Transforming gaming with AI-driven automation
    • Use case: toward autonomous testing
  • 4.2: Automate & Scale 4.2: Automate & Scale
    • 4.2: Automate & Scale
    • 4.2: Automate & Scale - an introduction
    • Autonomous testing of SaaS and Digital applications
    • Expedite mobile testing cycles
    • Use AI to assess how beautiful your app is
    • AI applied to Usability Testing
    • Experimenting with GPT3 to scale quality engineering
    • Self-healing RPA for Quality Engineering
  • 5: Manage data 5: Manage data
    • 5: Manage data
    • 5: Manage data - an introduction
    • The rise of synthetic data
    • An overview of synthetic data generation methods
    • Coverage requires a command of test data
    • Use case: a synthetic data platform for quality engineering
  • 6: Perform 6: Perform
    • 6: Perform
    • 6: Perform - an introduction
    • Representative performance tests based on real-life production traffic
    • AI-powered performance optimization
    • Use case: expediting performance testing with ai-based object identification
  • 7: Secure 7: Secure
    • 7: Secure
    • 7: Secure - an introduction
    • AI in security testing: why it matters
    • AI in security testing: Building trust with AI
    • Use cases of AI for security testing
    • Technology: an AI-driven security testing platform
  • 8: Operate 8: Operate
    • 8: Operate
    • 8: Operate - an introduction
    • The Rise of Intelligent Observability in Software Development
    • Smart impact analysis for SAP
    • From Logs to Tests: Usage-driven Regression Testing
    • Log mining for testing with AI
    • Industry 4.0: from point data to business impact
  • 9: Trust AI 9: Trust AI
    • 9: Trust AI
    • 9: Trust AI - an introduction
    • Quality engineering applied to artificial intelligence
    • Testing the ethics of AI
    • How to build AI that people trust
    • Scaling Quality Engineering Using AI for AI
    • Keep test criteria current using Federated Learning
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