AI-Powered QA Automation in CI/CD Training Course

Introduction to AI in QA Automation

• The role of AI in contemporary software testing.
• Comparing traditional versus AI-enhanced QA strategies.
• An overview of AI-based testing tools (e.g., Testim, mabl, Functionize).

Generating Tests with AI

• Model-based and UI-based test generation techniques.
• Utilising Testim or similar platforms for auto-generating test flows.
• Assessing test intent, stability, and reusability.

Regression Analysis and Test Prioritisation

• Impact-based test selection and pruning methods.
• Change-aware test execution for large-scale repositories.
• AI-driven prioritisation based on risk profiles and execution frequency.

Integration with CI/CD Pipelines

• Connecting automated tests to Jenkins, GitHub Actions, or GitLab CI.
• Implementing automated quality gating and test feedback loops.
• Triggering tests upon pull requests and deployment events.

Defect Prediction and Anomaly Detection

• Analysing test data to forecast potential failure areas.
• Clustering and triaging anomalies using machine learning techniques.
• Providing developers with actionable AI-generated insights.

Maintaining and Scaling AI-Based Tests

• Managing test drift and UI changes effectively.
• Managing version control and test configurations.
• Scaling QA environments to enterprise levels.

Case Studies and Real-World Applications

• Examples of enterprise-level AI QA pipeline implementations.
• Best practices for team adoption and strategic rollout.
• Key lessons learned: successes, challenges, and optimisation strategies.

Summary and Next Steps