Course Outline
Introduction to AI in Software Testing
- An overview of AI’s role and capabilities in testing and QA.
- An examination of AI tools commonly employed in modern test workflows.
- A discussion on the advantages and potential risks of AI-driven quality engineering.
LLMs for Test Case Generation
- Applying prompt engineering to generate unit and functional tests.
- Developing parameterized and data-driven test templates.
- Translating user stories and requirements into executable test scripts.
AI in Exploratory and Edge Case Testing
- Utilizing AI to identify untested branches or conditions.
- Simulating rare or abnormal usage scenarios.
- Implementing risk-based test generation strategies.
Automated UI and Regression Testing
- Using AI tools such as Testim or mabl for efficient UI test creation.
- Ensuring UI test stability through self-healing selectors.
- Conducting AI-based regression impact analysis following code changes.
Failure Analysis and Test Optimization
- Clustering test failures using LLMs or machine learning models.
- Minimizing flaky test runs and reducing alert fatigue.
- Prioritizing test execution based on historical data insights.
CI/CD Pipeline Integration
- Embedding AI test generation within Jenkins, GitHub Actions, or GitLab CI.
- Validating test quality during the pull request process.
- Implementing automation rollbacks and smart test gating within pipelines.
Future Trends and Responsible Use of AI 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.
Summary and Next Steps
Requirements
- Practical experience in software testing, test planning, or QA automation.
- Proficiency with testing frameworks such as JUnit, PyTest, or Selenium.
- Foundational knowledge of CI/CD pipelines and DevOps environments.
Target Audience
- QA engineers
- Software Development Engineers in Test (SDETs)
- Software testers operating within agile or DevOps environments
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3200 € + VAT*
Contact us for an exact quote and to hear our latest promotions
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
