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Duration 14 hours
Course Outline
Introduction to AI in QA Automation
- The function of AI in contemporary software testing
- Contrasting traditional QA strategies with AI-enhanced approaches
- Survey of AI-based testing tools (Testim, mabl, Functionize)
Test Generation via AI
- Model-based and UI-based test creation
- Utilizing Testim or comparable platforms for automatic workflow generation
- Assessing test intent, stability, and reusability
Regression Analysis and Test Prioritization
- Impact-driven test selection and reduction
- Change-aware test execution for extensive codebases
- AI-based prioritization derived from risk and usage frequency
CI/CD Pipeline Integration
- Linking automated tests with Jenkins, GitHub Actions, or GitLab CI
- Automated quality gating and test feedback cycles
- Initiating tests upon pull requests and deployment occurrences
Defect Forecasting and Anomaly Identification
- Examining test data to forecast probable failure zones
- Grouping and categorizing anomalies through ML techniques
- Providing feedback to developers via AI-generated insights
Maintaining and Scaling AI-Based Tests
- Managing test drift and UI modifications
- Version control and management of test configurations
- Expanding to enterprise-grade QA environments
Case Studies and Practical Applications
- Corporate implementations of AI QA pipelines
- Optimal strategies for team adoption and deployment
- Key takeaways: achievements, setbacks, and optimization
Recap and Future Directions
Requirements
- Hands-on experience with software testing or QA workflows
- Proficiency with CI/CD pipelines and DevOps methodologies
- Fundamental knowledge 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 management professionals
