Duration: 3-day Course

Course Description:

This intensive, hands-on course teaches participants how to leverage artificial intelligence and machine learning techniques to improve the efficiency, accuracy, and maintainability of regression testing. With a focus on real-world applications, the course combines foundational AI concepts with practical testing strategies. Participants will learn how to use AI to generate, prioritize, and maintain regression test cases, identify application changes, reduce false positives, and integrate these capabilities into modern CI/CD pipelines. Tools such as Testim, Applitools, and Mabl are explored alongside open-source frameworks.

Target Audience:

• QA Engineers and Automation Testers
• Software Developers in Test (SDET)
• DevOps Engineers and CI/CD Specialists
• Software Engineers with a focus on Quality Assurance
• Technical QA Leads and Engineering Managers

Key Takeaways:

• Understand how AI enhances the speed, accuracy, and maintainability of regression testing.
• Apply test impact analysis using ML models to prioritize and optimize test coverage.
• Use visual AI tools to detect UI changes and eliminate flaky test failures.
• Implement and automate AI-powered tests in modern DevOps workflows.
• Generate test cases from business requirements using NLP models.
• Evaluate the effectiveness, ROI, and limitations of AI in QA environments.

Day 1: Foundations of AI-Augmented Regression Testing

Module 1.1: The Evolution of Regression Testing

• Define regression testing and its critical role in software development.
• Identify challenges in traditional regression testing: test maintenance, coverage gaps, flaky tests.
• Introduce AI’s role in testing: self-healing, smart selectors, visual validation, intelligent test generation.

Module 1.2: Introduction to AI Concepts in Testing

• Review basic AI/ML concepts relevant to QA: supervised learning, classification, clustering.
• Understand how computer vision (CV), natural language processing (NLP), and anomaly detection apply to testing.
• Discuss model training, prediction, and feedback loops for test optimization.

Module 1.3: AI Testing Tool Landscape

• Overview of leading platforms:
  • Testim: AI-based UI test automation with self-healing and smart locators
  • Applitools: Visual AI for visual validation
  • Mabl: Intelligent end-to-end testing integrated with CI/CD
  • Open-source ML libraries (scikit-learn, PyCaret)

Hands-On Labs (Day 1):

• Lab 1: Setup and configure Testim and Applitools for a sample web application.
• Lab 2: Record and execute traditional vs AI-based regression test cases.
• Lab 3: Use Applitools to capture baseline UI snapshots and identify visual changes.

Day 2: Building AI-Driven Regression Testing Frameworks

Module 2.1: Intelligent Test Case Generation

• Generate test cases automatically using AI-driven techniques based on code analysis and usage patterns.
• Use NLP to translate requirements/user stories into test cases (e.g., convert Gherkin scenarios using GPT).
• Discuss synthetic test data generation using ML to expand test coverage.

Module 2.2: AI for Test Case Prioritization & Optimization

• Apply test impact analysis: using historical test data and code change frequency to prioritize execution.
• Train simple ML models to score and select relevant regression test cases.
• Identify high-risk paths using clustering and pattern detection.

Module 2.3: Visual Testing & Flaky Test Management

• Explain how visual AI engines detect layout shifts, missing elements, and pixel variations.
• Identify and auto-resolve flaky test issues with self-healing locators.
• Explore AI-powered DOM comparison and smart selector strategies.

Hands-On Labs (Day 2):

• Lab 4: Use ML to prioritize test cases based on change frequency and defect history.
• Lab 5: Generate test cases from user stories using NLP tools or ChatGPT API.
• Lab 6: Execute visual regression tests using Applitools; modify UI and observe auto-detection.

Day 3: Operationalizing AI Regression Testing in CI/CD Pipelines

Module 3.1: Self-Healing Test Automation

• Understand DOM monitoring and dynamic object recognition.
• Build resilient tests that adapt to UI changes without manual intervention.
• Configure fallback locators and auto-recovery logic.

Module 3.2: AI in CI/CD and DevOps Workflows

• Integrate AI-powered test suites into Jenkins, GitHub Actions, or GitLab pipelines.
• Automate regression test execution with every commit or nightly build.
• Collect and analyze regression test results through dashboards.

Module 3.3: Reporting, Ethics, and Limitations of AI in QA

• Generate actionable reports from AI test platforms with visual diff and heatmaps.
• Discuss false positives/negatives in AI-driven testing and interpretability of test results.
• Examine ethical considerations: AI biases, reliance risk, and human oversight.

Hands-On Labs (Day 3):

• Lab 7: Configure Jenkins pipeline to trigger Testim or Mabl regression tests.
• Lab 8: Create a self-healing test that adjusts to element name/class changes.
• Lab 9: Review test reports, perform root cause analysis using test logs and visual comparisons.