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Duration 14 hours
Course Outline
Introduction to Artificial Intelligence Applications in Software Quality Assurance
- Comprehensive review of AI capabilities within test engineering and quality assurance frameworks
- Classification of AI-driven tools utilized in contemporary testing workflows
- Assessment of strategic advantages and potential risks associated with AI-driven quality engineering
Utilization of Large Language Models for Test Case Development
- Application of prompt engineering techniques to generate unit and functional test cases
- Development of parameterized and data-driven test templates
- Transformation of user stories and formal requirements into executable test scripts
AI Applications in Exploratory Testing and Edge Case Validation
- Identification of untested code branches or conditional states through AI analysis
- Simulation of rare or non-standard usage scenarios
- Implementation of risk-based test generation methodologies
Automated User Interface and Regression Testing
- Deployment of AI tools, such as Testim or mabl, for the creation of user interface tests
- Ensuring test stability through the use of self-healing selectors
- Conducting AI-based regression impact analysis following code modifications
Failure Analysis and Test Process Optimization
- Clustering of test failures using Large Language Models or machine learning models
- Mitigation of unstable test executions and reduction of alert fatigue
- Prioritization of test execution cycles based on historical data insights
Integration into Continuous Integration/Continuous Delivery Pipelines
- Incorporation of AI test generation within Jenkins, GitHub Actions, or GitLab CI environments
- Verification of test quality during pull request reviews
- Implementation of automated rollbacks and intelligent test gating mechanisms in pipelines
Future Directions and Responsible AI Implementation in QA
- Evaluation of the accuracy and safety of AI-generated test artifacts
- Establishment of governance frameworks and audit trails for AI-enhanced testing processes
- Analysis of emerging trends in AI-QA platforms and intelligent observability
Summary of Key Concepts and Recommended Next Steps
Requirements
- Demonstrated experience in software testing, test planning, or quality assurance automation
- Proficiency with testing frameworks such as JUnit, PyTest, or Selenium
- Fundamental understanding 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 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
