AI-Augmented Test Generation and Quality Engineering Training Course
This course examines how artificial intelligence tools and large language models (LLMs) can optimize test coverage, accelerate test development, and elevate quality assurance practices.
Designed for intermediate QA professionals, this instructor-led live training (available online or onsite) focuses on automating and refining testing strategies through the application of AI-driven tools and methodologies.
Upon completion, participants will be equipped to:
- Leverage AI tools and prompt engineering to create unit, integration, and UI tests.
- Utilize LLMs for exploratory testing, uncovering edge cases, and conducting regression analysis.
- Apply AI-assisted triage to cluster and prioritize test failures and anomalies.
- Integrate AI-powered testing into CI/CD pipelines to boost release confidence.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live-lab environment.
Customization Options
- For tailored training requests, please get in touch to arrange a session.
Course Outline
Introduction to AI in Software Testing
- Overview of AI capabilities in testing and QA.
- Types of AI tools utilized in modern test workflows.
- Benefits and risks associated with AI-driven quality engineering.
LLMs for Test Case Generation
- Prompt engineering techniques for generating unit and functional tests.
- Creating parameterized and data-driven test templates.
- Converting user stories and requirements into executable 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 following 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
Requirements
- Background 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.
Target Audience
- QA engineers.
- Software Development Engineers in Test (SDETs).
- Software testers operating within agile or DevOps frameworks.
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
AI-Augmented Test Generation and Quality Engineering Training Course - Enquiry
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
Michal Maj - XL Catlin Services SE (AXA XL)
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