AI-Powered Software Testing Masterclass
Course Overview
Key Points
Use AI tools to generate high-quality test cases
Leverage GitHub Copilot for test script writing
Apply LLMs for requirement analysis and test planning
Build AI-assisted API and UI test suites
Detect and analyze test failures with AI insights
Reduce test maintenance with self-healing selectors
Integrate AI testing tools into CI/CD pipelines
Build a complete AI-augmented testing portfolio
Course Outline
Module 1: AI in Testing Landscape 2026
- How AI is changing software testing roles
- AI testing tools overview and comparison
- Prompt engineering for QA professionals
- Setting up AI-augmented testing environment
Module 2: AI-Assisted Test Case Design
- Using LLMs to analyze requirements and generate test scenarios
- Boundary value and equivalence partitioning with AI
- Risk-based test prioritization using AI analysis
- Validating AI-generated test cases for completeness
Module 3: Automated Test Script Generation
- GitHub Copilot and AI pair programming for tests
- Generating Selenium and Playwright scripts with AI
- AI-powered API test generation with Postman and REST
- Code review and quality checks on AI-generated code
Module 4: Self-Healing and Intelligent Test Execution
- Self-healing selectors and dynamic locator strategies
- Visual AI testing and screenshot comparison
- AI-driven test impact analysis
- Flaky test detection and root cause analysis
Module 5: AI in CI/CD and Reporting
- Integrating AI testing tools in Jenkins and GitHub Actions
- AI-powered defect classification and reporting
- Test coverage insights with AI analytics
- Shift-left testing with AI assistance
Module 6: Capstone — AI Testing Project
- Full AI-augmented test suite for a real application
- Performance and security testing with AI tools
- Portfolio documentation and presentation
- Future of AI in QA and career roadmap
Ahsan Akhtar
Senior Test Automation Engineer & AI Trainer
This Course Includes
Duration 6-8 weeks (Weekend Classes Only)
Courses
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