AI-Augmented Test Generation and Quality Engineering

Prerequisites:

• Experience in software testing, test planning, or QA automation
• Familiarity with testing frameworks such as JUnit, PyTest, or Selenium
• Basic understanding of CI/CD pipelines and DevOps environments

Audience

• QA engineers
• Software Development Engineers in Test (SDETs)
• Software testers working in agile or DevOps settings

Overview:

AI-Augmented Test Generation and Quality Engineering explores how artificial intelligence tools and large language models (LLMs) can improve test coverage, speed up test creation, and enhance quality assurance processes.

This instructor-led, live training (online or onsite) is aimed at intermediate-level QA professionals who wish to automate and enhance their testing strategies using AI-driven tools and practices.

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

• Generate unit, integration, and UI tests using AI tools and prompts.
• Use LLMs for exploratory testing, edge-case discovery, and regression analysis.
• Cluster and prioritize test failures and anomalies using AI-assisted triage.
• Integrate AI-based testing into CI/CD pipelines to improve release confidence.

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.

Course Outline:

Introduction to AI in Software Testing

• Overview of AI capabilities in testing and QA
• Types of AI tools used in modern test workflows
• Benefits and risks of AI-driven quality engineering

LLMs for Test Case Generation

• Prompt engineering for generating unit and functional tests
• Creating parameterized and data-driven test templates
• Converting user stories and requirements into test scripts

AI in Exploratory and Edge Case Testing

• Identifying untested branches or conditions using AI
• Simulating rare or abnormal usage scenarios
• Risk-based test generation strategies

Automated UI and Regression Testing

• Using AI tools like Testim or mabl for UI test creation
• Maintaining stable UI tests through self-healing selectors
• AI-based regression impact analysis after code changes

Failure Analysis and Test Optimization

• Clustering test failures using LLM or ML models
• Reducing flaky test runs and alert fatigue
• Prioritizing test execution based on historical insights

CI/CD Pipeline Integration

• Embedding AI test generation in Jenkins, GitHub Actions, or GitLab CI
• Validating test quality during pull requests
• Automation rollbacks and smart test gating in pipelines

Future Trends and Responsible Use of AI in QA

• Evaluating the accuracy and safety of AI-generated tests
• Governance and audit trails for AI-enhanced test processes
• Trends in AI-QA platforms and intelligent observability

Summary and Next Steps