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Duration 14 hours
Course Outline
Introduction to AI in QA Automation
- The role of AI in contemporary software testing
- Contrasting traditional vs. AI-enhanced QA strategies
- Overview of AI-based testing tools (Testim, mabl, Functionize)
AI-Assisted Test Generation
- Model-based and UI-driven test creation
- Auto-generating workflows using Testim or similar platforms
- Assessing test intent, stability, and reusability
Regression Analysis and Test Prioritization
- Impact-driven test selection and pruning
- Change-aware test execution for large repositories
- AI-based prioritization grounded in risk and frequency
CI/CD Pipeline Integration
- Linking automated tests to Jenkins, GitHub Actions, or GitLab CI
- Automated quality gating and test feedback loops
- Triggering tests upon pull requests and deployment events
Defect Forecasting and Anomaly Detection
- Analyzing test data to anticipate potential failure areas
- Clustering and triaging anomalies via ML techniques
- Providing developers with AI-generated insights
Managing and Scaling AI-Based Tests
- Addressing test drift and UI modifications
- Version control and test configuration management
- Scaling to enterprise-grade QA environments
Case Studies and Practical Applications
- Enterprise-level AI QA pipeline implementations
- Best practices for team adoption and rollout
- Key takeaways: successes, failures, and optimization
Summary and Future Directions
Requirements
- Practical experience with software testing or QA workflows
- Familiarity with CI/CD pipelines and DevOps practices
- Foundational knowledge of automated testing tools or frameworks
Target Audience
- QA leads and test automation engineers
- DevOps professionals and SREs
- Agile testers and quality managers
