AI-Powered QA Automation in CI/CD

• Experience with software testing or QA workflows
• Familiarity with CI/CD pipelines and DevOps practices
• Basic understanding of automated testing tools or frameworks

Audience

• QA leads and test automation engineers
• DevOps professionals and SREs
• Agile testers and quality managers

AI-powered QA automation enhances traditional testing by generating smart test cases, optimizing regression coverage, and integrating intelligent quality gates into CI/CD pipelines for scalable and reliable software delivery.

This instructor-led, live training (online or onsite) is aimed at intermediate-level QA and DevOps professionals who wish to apply AI tools to automate and scale quality assurance in continuous integration and deployment workflows.

By the end of this training, participants will be able to:

• Generate, prioritize, and maintain tests using AI-driven automation platforms.
• Integrate intelligent QA gates into CI/CD pipelines to prevent regressions.
• Use AI for exploratory testing, defect prediction, and test flakiness analysis.
• Optimize testing time and coverage across fast-moving agile projects.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to AI in QA Automation

• Role of AI in modern software testing
• Comparison of traditional vs. AI-enhanced QA strategies
• Overview of AI-based testing tools (Testim, mabl, Functionize)

Generating Tests with AI

• Model-based and UI-based test generation
• Using Testim or similar platforms to auto-generate flows
• Evaluating test intent, stability, and reusability

Regression Analysis and Test Prioritization

• Impact-based test selection and pruning
• Change-aware test runs for large repositories
• AI-driven prioritization based on risk and frequency

Integration with CI/CD Pipelines

• Connecting automated tests to Jenkins, GitHub Actions, or GitLab CI
• Automated quality gating and test feedback loops
• Triggering tests on pull requests and deployment events

Defect Prediction and Anomaly Detection

• Analyzing test data to predict likely failure areas
• Clustering and triaging anomalies using ML techniques
• Feedback to developers using AI-generated insights

Maintaining and Scaling AI-Based Tests

• Dealing with test drift and UI changes
• Version control and test configuration management
• Scaling 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, failures, and tuning

Summary and Next Steps