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Duration 14 hours
Course Outline
Foundations of AI in Software Testing
- Overview of AI applications in testing and quality assurance.
- Identification of AI tools prevalent in contemporary test workflows.
- Analysis of the advantages and potential risks associated with AI-driven quality engineering.
Utilizing LLMs for Test Case Creation
- Applying prompt engineering techniques 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
- Using AI to identify untested code branches or logical conditions.
- Simulating rare or abnormal usage scenarios for robustness testing.
- Implementing risk-based strategies for test generation.
Automated UI and Regression Testing
- Employing AI platforms like Testim or mabl for UI test development.
- Ensuring UI test stability through self-healing selectors.
- Conducting AI-based regression impact analysis following code modifications.
Failure Analysis and Test Optimization
- Clustering test failures using LLM or ML models.
- Minimizing flaky test runs 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.
- Validating test quality during the pull request phase.
- Implementing automation rollbacks and smart test gating mechanisms in pipelines.
Future Trends and Responsible AI Usage in QA
- Assessing the accuracy and security of AI-generated tests.
- Establishing governance and audit trails for AI-enhanced testing processes.
- Exploring trends in AI-QA platforms and intelligent observability.
Conclusion and Path Forward
Requirements
- Practical experience in software testing, test planning, or QA automation.
- Proficiency with common testing frameworks like JUnit, PyTest, or Selenium.
- Foundational knowledge of CI/CD pipelines and DevOps ecosystems.
Target Audience
- QA Engineers
- Software Development Engineers in Test (SDETs)
- Software testers operating within Agile or DevOps environments
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
