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Duration 14 hours
Course Outline
AI in Software Testing: An Introduction
- Overview of AI’s role in testing and QA landscapes
- Categories of AI tools employed in contemporary test workflows
- Advantages and potential risks associated with AI-driven quality engineering
Generating Test Cases with LLMs
- Prompt engineering techniques for creating 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 Testing
- Detecting untested branches or conditions through AI analysis
- Simulating rare or irregular usage scenarios
Automating UI and Regression Testing
- Employing AI tools such as Testim or mabl for UI test development
- Performing AI-based regression impact analysis following code modifications
Failure Analysis and Test Optimization
- Aggregating test failures using LLM or ML models
- Minimizing flaky test executions and alert overload
- Optimizing test execution order based on historical data insights
Integration into CI/CD Pipelines
- Incorporating AI test generation into Jenkins, GitHub Actions, or GitLab CI
- Assessing test quality during the pull request phase
- Implementing automated rollbacks and intelligent test gating within pipelines
Future Trends and Ethical AI Usage in QA
- Assessing the precision and security of AI-generated tests
- Emerging trends in AI-QA platforms and intelligent observability
Conclusions and Future Directions
Requirements
- Prior experience in software testing, test planning, or QA automation
- Basic familiarity with CI/CD pipelines and DevOps ecosystems
Target Audience
- QA Engineers
- Software Development Engineers in Test (SDETs)
- Software testers operating in 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
