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Duration 14 hours
Course Outline
Introductory Overview: AI in Software Testing
- Examining the capabilities of AI within testing and QA landscapes
- Identifying various AI tools utilized in contemporary test workflows
- Assessing the advantages and potential risks of AI-driven quality engineering
Leveraging 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 Applications in Exploratory and Edge Case Testing
- Using AI to detect untested branches or specific conditions
- Modeling rare or abnormal usage scenarios
- Implementing risk-based strategies for test generation
Automated UI and Regression Testing
- Employing AI tools such as Testim or mabl to create 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 utilizing LLM or ML models
- Mitigating flaky test executions and reducing alert fatigue
- Prioritizing test runs by analyzing historical data insights
Integration into CI/CD Pipelines
- Embedding AI test generation within Jenkins, GitHub Actions, or GitLab CI
- Verifying test quality during the pull request stage
- Implementing automation rollbacks and intelligent test gating in pipelines
Emerging Trends and Responsible AI Use in QA
- Assessing the precision and safety of AI-generated tests
- Establishing governance and audit trails for AI-enhanced testing processes
- Exploring trends in AI-QA platforms and intelligent observability
Concluding Summary and Future Actions
Requirements
- Professional background in software testing, test planning, or QA automation
- Proficiency with testing frameworks such as JUnit, PyTest, or Selenium
- Fundamental knowledge of CI/CD pipelines and DevOps environments
Target Audience
- QA Engineers
- Software Development Engineers in Test (SDETs)
- Testers operating within agile or DevOps teams
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
