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Duration 14 hours
Course Outline
Intro to AI in QA Automation
- The impact of AI on contemporary software testing
- Contrasting conventional vs. AI-augmented QA approaches
- Survey of AI-centric testing platforms (Testim, mabl, Functionize)
AI-Assisted Test Creation
- Test generation based on models and user interfaces
- Utilizing Testim or comparable tools for automatic flow generation
- Assessing test intent, consistency, and reusability
Regression Insights and Test Ranking
- Selecting and refining tests based on impact
- Executing change-aware tests in extensive codebases
- AI-based prioritization driven by risk and execution frequency
CI/CD Pipeline Integration
- Linking automated tests with Jenkins, GitHub Actions, or GitLab CI
- Implementing automated quality checks and feedback mechanisms
- Initiating tests upon pull requests and deployment triggers
Defect Forecasting and Anomaly Identification
- Reviewing test data to anticipate potential failure points
- Grouping and categorizing anomalies via ML methods
- Providing developers with AI-derived insights
Sustaining and Expanding AI-Based Testing
- Managing test drift and UI modifications
- Handling version control and test configuration oversight
- Scaling solutions for enterprise-grade QA settings
Real-World Case Studies
- Corporate deployment of AI QA pipelines
- Best practices for team integration and rollout
- Key takeaways: achievements, challenges, and optimization
Conclusion and Future Pathways
Requirements
- Prior exposure to 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 specialists
- DevOps engineers and Site Reliability Engineers (SREs)
- Agile testers and quality assurance managers
