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
Overview of AI in Software Testing
- Capabilities of AI within testing and QA landscapes
- Categories of AI tools prevalent in modern test workflows
- Advantages and potential risks associated with AI-driven quality engineering
Leveraging LLMs for Test Case Creation
- Prompt engineering techniques for generating unit and functional tests
- Developing parameterized and data-driven test templates
- Translating user stories and requirements into executable test scripts
AI Applications in Exploratory and Edge Case Testing
- Utilizing AI to identify untested branches or conditional logic
- Simulating infrequent or abnormal usage scenarios
- Implementing risk-based test generation strategies
Automation of UI and Regression Testing
- Employing AI tools such as Testim or mabl for UI test development
- Ensuring UI test stability via self-healing selectors
- Conducting AI-based regression impact analysis following code modifications
Failure Analysis and Test Process Optimization
- Grouping test failures using LLM or ML models
- Minimizing flaky test executions and reducing alert fatigue
- Prioritizing test execution based on historical data insights
Integration into CI/CD Pipelines
- Embedding AI test generation within Jenkins, GitHub Actions, or GitLab CI
- Verifying test quality during the pull request phase
- Implementing automation rollbacks and intelligent test gating mechanisms
Emerging Trends and Responsible AI Adoption in QA
- Assessing the accuracy and safety of AI-generated tests
- Establishing governance and audit trails for AI-enhanced test processes
- Exploring trends in AI-QA platforms and intelligent observability
Recap and Future Directions
Requirements
- Practical experience in software testing, test planning, or QA automation
- Knowledge of testing frameworks such as JUnit, PyTest, or Selenium
- Foundational understanding of CI/CD pipelines and DevOps environments
Intended Audience
- QA engineers
- Software Development Engineers in Test (SDETs)
- Software testers operating in agile or DevOps contexts
Testimonials (2)
The session was highly interactive and applicable to the business.
Jorge Boscan - Chevron Global Technology Services Company
Course - Advanced GitHub Copilot & AI for Projects and Infrastructure
Machine Translated
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
