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Duration 14 hours
Course Outline
Foundations of AI in QA Automation
- The function of AI in contemporary software testing
- Review of AI-based testing solutions (Testim, mabl, Functionize)
AI-Driven Test Generation
- Test creation via model-based and UI-based approaches
- Utilizing Testim or comparable platforms to automatically generate workflows
- Assessing test intent, stability, and reusability
Regression Assessment and Test Prioritization
- Impact-driven test selection and pruning
- AI-based prioritization grounded in risk and frequency metrics
CI/CD Pipeline Integration
- Linking automated tests with Jenkins, GitHub Actions, or GitLab CI
- Automated quality gating and iterative test feedback mechanisms
- Initiating tests upon pull requests or deployment events
Defect Prediction and Anomaly Identification
- Examining test data to forecast potential failure points
- Clustering and triaging anomalies using machine learning methods
- Providing developers with insights generated by AI
Maintenance and Scaling of AI-Based Tests
- Managing test drift and UI modifications
- Version control and test configuration administration
- Expanding to enterprise-scale QA environments
Case Studies and Practical Applications
- Enterprise-level implementations of AI QA pipelines
- Best practices for team adoption and rollout strategies
- Key takeaways: successes, challenges, and optimization insights
Recap and Future Steps
Requirements
- Practical experience with software testing or QA processes
Target Audience
- QA leaders and test automation engineers
- DevOps engineers and Site Reliability Engineers (SREs)
- Agile testers and quality management professionals
