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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 versus AI-augmented QA strategies
- Overview of AI-based testing tools (Testim, mabl, Functionize)
Generating Tests with AI
- Model-driven and UI-driven test generation
- 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
- AI-driven prioritization based on risk and frequency
Integration with CI/CD Pipelines
- Linking automated tests to Jenkins, GitHub Actions, or GitLab CI
Defect Prediction and Anomaly Detection
- Analyzing test data to anticipate potential failure points
- Clustering and triaging anomalies using ML techniques
- Providing developers with insights generated by AI
Maintaining and Scaling AI-Based Tests
- Managing test drift and UI modifications
- Version control and test configuration management
- Scaling to enterprise-level QA environments
Case Studies and Real-World Applications
- Enterprise implementations of AI QA pipelines
- Lessons learned: successes, failures, and tuning
Summary and Next Steps
Requirements
- Hands-on experience with software testing or QA processes
Target Audience
- QA leads and test automation specialists
- DevOps engineers and SREs
- Agile testers and quality managers
