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Course Outline
Module 1: Introduction to AI for QA
- What constitutes Artificial Intelligence?
- Distinctions between Machine Learning, Deep Learning, and Rule-based Systems
- The progression of software testing with AI integration
- Primary advantages and obstacles of AI in QA
Module 2: Data and ML Basics for Testers
- Differentiating structured from unstructured data
- Concepts of features, labels, and training datasets
- Supervised versus unsupervised learning approaches
- Introduction to model assessment metrics (accuracy, precision, recall, etc.)
- Practical QA datasets
Module 3: AI Use Cases in QA
- AI-driven test case generation
- Defect prediction utilizing ML
- Test prioritization and risk-based testing
- Visual testing via computer vision
- Log analysis and anomaly detection
- Natural language processing (NLP) for test scripts
Module 4: AI Tools for QA
- Survey of AI-enabled QA platforms
- Utilizing open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototypes
- Overview of LLMs in test automation
- Creating a basic AI model to predict test failures
Module 5: Integrating AI into QA Workflows
- Assessing the AI-readiness of your QA processes
- Continuous integration and AI: embedding intelligence into CI/CD pipelines
- Designing intelligent test suites
- Handling AI model drift and retraining cycles
- Ethical considerations in AI-powered testing
Module 6: Hands-on Labs and Capstone Project
- Lab 1: Automate test case generation using AI
- Lab 2: Build a defect prediction model using historical test data
- Lab 3: Use an LLM to review and optimize test scripts
- Capstone: End-to-end implementation of an AI-powered testing pipeline
Requirements
Participants are expected to possess:
- Over 2 years of experience in software testing or QA positions
- Familiarity with test automation platforms (e.g., Selenium, JUnit, Cypress)
- Fundamental programming knowledge (preferably in Python or JavaScript)
- Experience with version control and CI/CD systems (e.g., Git, Jenkins)
- No previous AI/ML background is necessary, though curiosity and a willingness to experiment are vital
21 Hours
Testimonials (3)
hands on exercises, easier to retain information
ashley bolen - Insurance Corporation of British Columbia
Course - Test Automation with Selenium
Key topics can be discussed and agreed upon with the trainer in advance. Relaxed and pleasant atmosphere during the seminar days.
Lorenz - Continentale Lebensversicherung AG
Course - Advanced Selenium
I gained new knowledge and I'm pretty confident about it. Nothing unclear.
