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Duration 14 hours
Course Outline
Introduction to AI in QA Automation
- The function of AI in contemporary software testing
- Analyzing traditional versus AI-enhanced QA strategies
- Survey of AI-based testing tools (Testim, mabl, Functionize)
AI-Driven Test Generation
- Model-based and UI-based test creation
- Utilizing Testim or similar platforms for automatic flow generation
- Assessing test intent, stability, and reusability
Regression Analysis and Test Prioritization
- Selecting and pruning tests based on impact
- Change-aware test execution for extensive repositories
- AI-based prioritization driven by risk and frequency
CI/CD Pipeline Integration
- Linking automated tests with Jenkins, GitHub Actions, or GitLab CI
- Automated quality gating and iterative test feedback
- Initiating tests upon pull requests and deployment events
Defect Prediction and Anomaly Detection
- Examining test data to forecast probable failure zones
- Clustering and categorizing anomalies via ML techniques
- Providing developers with AI-generated insights
Maintenance and Scaling of AI-Based Tests
- Addressing test drift and UI modifications
- Managing version control and test configurations
- Scaling for enterprise-grade QA environments
Case Studies and Practical Applications
- Enterprise-level implementations of AI QA pipelines
- Best practices for team adoption and deployment
- Key takeaways: successes, challenges, and optimization
Wrap-Up and Future Directions
Requirements
- Background in software testing or QA processes
- Knowledge of CI/CD pipelines and DevOps methodologies
- Fundamental grasp of automated testing tools or frameworks
Target Audience
- QA leads and test automation specialists
- DevOps engineers and SREs
- Agile testers and quality managers
