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Duration 14 hours
Course Outline
Introduction to AI in QA Automation
- The role of AI in contemporary software testing
- Comparing traditional versus AI-enhanced QA strategies
- Overview of AI-based testing tools (Testim, mabl, Functionize)
Generating Tests with AI
- Model-based and UI-based test generation techniques
- Utilizing Testim or similar platforms to auto-generate workflows
- Assessing test intent, stability, and reusability
Regression Analysis and Test Prioritization
- Impact-based test selection and pruning methods
- Change-aware test execution for large codebases
- AI-driven prioritization based on risk and 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 on pull requests and deployment events
Defect Prediction and Anomaly Detection
- Analyzing test data to forecast potential failure areas
- Clustering and triaging anomalies using machine learning techniques
- Providing developers with AI-generated insights
Maintaining and Scaling AI-Based Tests
- Managing test drift and UI modifications
- Version control and test configuration management
- Scaling AI-based tests to enterprise-level QA environments
Case Studies and Real-World Applications
- Enterprise implementations of AI QA pipelines
- Best practices for team adoption and rollout
- Lessons learned: successes, challenges, and optimization
Summary and Next Steps
Requirements
- Practical experience with software testing or QA processes
- Knowledge of CI/CD pipelines and DevOps methodologies
- Foundational understanding of automated testing tools or frameworks
Target Audience
- QA leads and test automation engineers
- DevOps specialists and Site Reliability Engineers (SREs)
- Agile testers and quality managers
