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Duration 14 hours
Course Outline
Introduction to AI in Software Testing
- An overview of AI capabilities within testing and QA landscapes.
- Identification of AI tools prevalent in modern test workflows.
- Analysis of the benefits and potential risks associated with AI-driven quality engineering.
LLMs for Test Case Generation
- Prompt engineering techniques for generating unit and functional tests.
- Development of 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 complex conditions.
- Simulating rare or abnormal usage scenarios to test robustness.
- Implementing risk-based strategies for test generation.
Automated UI and Regression Testing
- Leveraging AI tools such as Testim or mabl for efficient UI test creation.
- Ensuring stable UI tests through self-healing selector technologies.
- Conducting AI-based regression impact analysis following code modifications.
Failure Analysis and Test Optimization
- Clustering test failures using LLM or machine learning models.
- Mitigating flaky test runs and reducing alert fatigue.
- Prioritizing test execution by leveraging historical 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 mechanisms in 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
- Professional experience in software testing, test planning, or QA automation.
- Proficiency with popular testing frameworks such as JUnit, PyTest, or Selenium.
- Fundamental knowledge of CI/CD pipelines and DevOps environments.
Target Audience
- Quality Assurance Engineers.
- Software Development Engineers in Test (SDETs).
- Software testers operating within agile or DevOps frameworks.
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
