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Duration 14 hours
Course Outline
Introduction to AI in QA Automation
- The function of AI in contemporary software testing
- Contrasting conventional QA strategies with AI-enhanced approaches
- Survey of AI-based testing solutions (Testim, mabl, Functionize)
Test Generation via AI
- Model-based and UI-based test creation
- Utilizing Testim or comparable platforms for automated flow generation
- Assessing test intent, stability, and reusability
Regression Analysis and Test Prioritization
- Impact-focused test selection and reduction
- Change-aware test execution for extensive repositories
- AI-based prioritization driven by risk and usage frequency
CI/CD Pipeline Integration
- Linking automated tests to Jenkins, GitHub Actions, or GitLab CI
- Automated quality gating and test feedback mechanisms
- Activating tests upon pull requests and deployment triggers
Defect Prediction and Anomaly Detection
- Examining test data to anticipate probable failure points
- Clustering and categorizing anomalies using ML techniques
- Providing developers with AI-generated 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 deployment
- Key takeaways: successes, challenges, and optimization
Conclusion and Future Steps
Requirements
- Prior experience with software testing or QA processes
- Knowledge of CI/CD pipelines and DevOps methodologies
- Fundamental understanding of automated testing tools or frameworks
Target Audience
- QA leads and test automation engineers
- DevOps professionals and Site Reliability Engineers (SREs)
- Agile testers and quality managers
