1. AI-Based Oracles in Manual Testing:

Learn how AI-powered tools automatically generate expected results, validate test completeness, and convert requirements into testable assertions to enhance manual testing effectiveness.

2. AI-Based Oracles in Automated Testing:

Discover how machine learning models dynamically generate assertions, detect anomalies, and create self-healing test scripts that reduce maintenance and improve reliability.

3. Techniques for Automated Oracle Generation:

Explore differential testing, invariant detection, mutation analysis, and anomaly detection techniques to automatically generate comprehensive test oracles.

4. Data Generation for Manual Tests:

Master template-based approaches, tool-assisted generation, and data masking techniques to create realistic, privacy-compliant test data for manual testing.

5. Data Generation for Automated Tests:

Learn programmatic data generation using factory patterns, database seeding, and AI techniques that integrate with CI/CD pipelines while maintaining data integrity.

6. Using AI to Generate Test Data:

Explore how GANs, VAEs, and LLMs create realistic synthetic test data that preserves statistical properties, discovers edge cases, and maintains privacy.

7. Using RAG for Context in Manual Testing:

Discover how Retrieval-Augmented Generation provides instant, contextual answers by combining AI with your documentation to enhance testing decisions.

8. Data Masking with AI:

Learn how AI automatically discovers and masks sensitive data using NLP, context-aware techniques, and synthetic generation while maintaining utility for testing.