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Duration 14 hours
Course Outline
Foundations of AI in Software Testing
- A broad look at AI’s role in testing and QA landscapes.
- Categorization of AI tools employed in contemporary test workflows.
- Analysis of the advantages and potential risks of AI-driven quality engineering.
Leveraging LLMs for Test Case Authoring
- 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 for Exploratory and Edge Case Verification
- Using AI to pinpoint untested code branches or conditional logic.
- Simulating unusual or aberrant usage scenarios.
- Implementing risk-oriented test generation approaches.
Streamlining UI and Regression Testing
- Utilizing AI platforms like Testim or mabl for UI test creation.
- Ensuring UI test stability via self-healing selectors.
- Performing AI-based regression impact assessments following code modifications.
Failure Diagnostics and Test Efficiency
- Grouping test failures using LLM or Machine Learning models.
- Minimizing flaky test executions and reducing alert noise.
- Optimizing test execution order based on historical performance data.
Integrating with CI/CD Pipelines
- Incorporating AI test generation into Jenkins, GitHub Actions, or GitLab CI.
- Verifying test quality during the pull request phase.
- Implementing automated rollbacks and intelligent test gating within pipelines.
Emerging Trends and Ethical AI Use in QA
- Assessing the accuracy and safety of AI-generated test suites.
- Establishing governance frameworks and audit trails for AI-enhanced processes.
- Exploring advancements in AI-QA platforms and intelligent observability.
Recap and Forward-Looking Recommendations
Requirements
- Practical experience in software testing, test strategy planning, or QA automation.
- Proficiency with popular testing frameworks such as JUnit, PyTest, or Selenium.
- Foundational knowledge of CI/CD pipelines and DevOps ecosystems.
Target Learners
- QA Engineers
- Software Development Engineers in Test (SDETs)
- Testers operating within Agile or DevOps contexts
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
