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Duration 14 hours
Course Outline
Introduction to AI in Software Testing
- Understanding the capabilities of AI within testing and QA landscapes.
- Identifying AI tools that support modern test workflows.
- Assessing the advantages and potential risks of AI-driven quality engineering.
Generating Test Cases with LLMs
- Crafting prompts to generate unit and functional tests efficiently.
- Designing parameterized and data-driven test templates.
- Transforming user stories and requirements into executable test scripts.
AI for Exploratory and Edge Case Testing
- Leveraging AI to spot untested branches or conditions.
- Replicating rare or abnormal usage scenarios for thorough testing.
- Implementing risk-based strategies for test generation.
Automated UI and Regression Testing
- Employing AI platforms like Testim or mabl to build UI tests.
- Ensuring UI test stability via self-healing selectors.
- Conducting AI-based regression impact analysis following code updates.
Failure Analysis and Test Optimization
- Grouping test failures using LLMs or ML models.
- Mitigating flaky tests and reducing alert fatigue.
- Prioritizing test execution by analyzing historical data.
Integration into CI/CD Pipelines
- Integrating AI test generation into Jenkins, GitHub Actions, or GitLab CI.
- Verifying test quality during the pull request process.
- Implementing smart test gating and automation rollbacks in pipelines.
Future Trends and Responsible AI Use in QA
- Assessing the accuracy and safety of AI-generated tests.
- Establishing governance and audit trails for AI-enhanced processes.
- Exploring trends in AI-QA platforms and intelligent observability.
Summary and Next Steps
Requirements
- 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
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
