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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 approaches
- Review of AI-based testing platforms (Testim, mabl, Functionize)
AI-Assisted Test Generation
- Model-based and UI-driven test creation
- Utilizing Testim or comparable platforms to auto-generate workflows
- Assessing test intent, stability, and reusability
Regression Analysis and Test Prioritization
- Impact-based test selection and reduction
- Change-aware test execution for extensive repositories
- AI-led prioritization based on risk and frequency
CI/CD Pipeline Integration
- Linking automated tests to Jenkins, GitHub Actions, or GitLab CI
- Automated quality gates and test feedback cycles
- Activating tests upon pull requests and deployment events
Defect Forecasting and Anomaly Identification
- Examining test data to anticipate probable failure points
- Clustering and categorizing anomalies using ML methods
- Providing developers with AI-derived insights
Maintenance and Scaling of AI-Based Tests
- Managing test drift and UI modifications
- Version control and test configuration governance
- Scaling to enterprise-grade QA environments
Case Studies and Practical Applications
- Enterprise-level implementation of AI QA pipelines
- Best practices for team adoption and rollout
- Key takeaways: successes, challenges, and optimization
Recap and Future Directions
Requirements
- Practical experience with software testing or QA processes
- Knowledge of CI/CD pipelines and DevOps methodologies
- Fundamental grasp of automated testing tools or frameworks
Target Audience
- QA leaders and test automation specialists
- DevOps experts and Site Reliability Engineers (SREs)
- Agile testers and quality managers
