Course Overview
The GenAI for Testers course by Stalwart Learning empowers software testers to leverage Generative AI for improving testing efficiency and effectiveness. This course introduces AI-powered tools and techniques to enhance test case generation, automate test execution, identify defects faster, and optimize testing strategies. Testers will explore the integration of AI models with testing frameworks, predictive analytics for test optimization, and how to incorporate AI into Continuous Integration/Continuous Delivery (CI/CD) pipelines. Participants will be ready to adopt GenAI in both manual and automated testing scenarios to ensure high-quality software delivery.
Prerequisites
- Basic understanding of software testing principles and practices.
- Familiarity with testing tools and frameworks like Selenium, JUnit, or TestNG is beneficial.
- No prior experience with AI is required, but knowledge of automation and scripting is helpful.
Course Outline
1. Introduction to GenAI in Software Testing
- Overview of Generative AI and its application in testing
- Understanding how AI can enhance the testing lifecycle
- Benefits of using AI in manual and automated testing
2. AI-Driven Test Case Generation
- Using AI models to automatically generate test cases based on requirements
- Enhancing test coverage and identifying edge cases with AI
- Generating functional, regression, and performance test cases using GenAI
3. Automated Test Execution with GenAI
- Integrating AI-powered tools into existing test automation frameworks
- Automating test execution with minimal input using AI
- Using machine learning to optimize test execution for faster results
4. Defect Detection and Root Cause Analysis with AI
- Leveraging AI for identifying defects in code and test cases
- Using machine learning models to automatically categorize defects and suggest fixes
- Accelerating root cause analysis using AI-driven insights
5. Test Optimization and Maintenance with GenAI
- Optimizing test suites using AI for better coverage and performance
- Automating the maintenance of test scripts as applications evolve
- Using AI to prioritize tests based on risk and code changes
6. AI in Continuous Integration/Continuous Delivery (CI/CD) Pipelines
- Integrating AI into CI/CD workflows for automated testing and faster feedback
- Using GenAI to trigger tests based on code commits and pull requests
- Leveraging AI to improve feedback loops and reduce testing bottlenecks
7. Predictive Analytics for Testing
- Using AI for predictive testing to anticipate defects and failures
- Analyzing past test results to predict future testing needs and focus areas
- Leveraging GenAI for test coverage analysis and gap detection
8. Ethical Considerations and Challenges of AI in Testing
- Addressing potential biases and ethical challenges in AI-driven testing
- Ensuring the transparency and accountability of AI tools in testing
- Mitigating risks and maintaining control over AI-powered decisions in testing
9. Practical Use Cases and Real-World Applications
- Hands-on exercises with AI tools for test automation and case generation
- Real-world examples of AI-enhanced testing in agile and DevOps environments
- Case studies on successful adoption of GenAI in testing processes