Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Introduction to AI in QA Automation
- The impact of AI on contemporary software testing
- Contrasting traditional versus AI-enhanced QA strategies
- Survey of AI-based testing solutions (Testim, mabl, Functionize)
Test Generation via AI
- Model-driven 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
- Selecting and pruning tests based on code impact
- Change-aware test execution for extensive codebases
- AI-based prioritization considering risk and execution frequency
CI/CD Pipeline Integration
- Linking automated tests with Jenkins, GitHub Actions, or GitLab CI
- Implementing automated quality gates and feedback mechanisms
- Initiating tests via pull requests and deployment triggers
Defect Forecasting and Anomaly Identification
- Examining test data to anticipate probable failure points
- Clustering and triaging anomalies through machine learning
- Providing developer insights generated by AI analysis
Maintenance and Scaling of AI-Based Tests
- Managing test drift and UI modifications
- Version control and configuration management for tests
- Expanding to enterprise-grade QA infrastructures
Case Studies and Practical Applications
- Enterprise case studies involving AI QA pipelines
- Best practices for team adoption and rollout
- Key takeaways: successes, challenges, and optimization
Conclusion and Future Directions
Requirements
- Practical experience in software testing or QA processes
- Working 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 engineers and SREs
- Agile testers and quality managers
