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Course Outline
Module 1: Introduction to AI for QA
- Defining Artificial Intelligence
- Distinguishing between Machine Learning, Deep Learning, and Rule-based Systems
- The progression of software testing in the context of AI
- Primary advantages and obstacles of implementing AI in QA
Module 2: Data and ML Fundamentals for Testers
- Differentiating structured from unstructured data
- Grasping features, labels, and training datasets
- Exploring supervised and unsupervised learning paradigms
- Introduction to model evaluation metrics (accuracy, precision, recall, etc.)
- Analyzing real-world QA datasets
Module 3: AI Use Cases in QA
- AI-driven test case generation
- Predicting defects using machine learning
- Test prioritization and risk-based testing strategies
- Visual testing utilizing computer vision
- Log analysis and anomaly detection techniques
- Applying Natural Language Processing (NLP) to test scripts
Module 4: AI Tools for QA
- Surveying AI-enabled QA platforms
- Leveraging open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) to build QA prototypes
- Introduction to Large Language Models (LLMs) in test automation
- Constructing a basic AI model to forecast test failures
Module 5: Integrating AI into QA Workflows
- Assessing the AI-readiness of existing QA processes
- Continuous integration with AI: embedding intelligence into CI/CD pipelines
- Designing intelligent test suites
- Handling AI model drift and managing retraining cycles
- Ethical implications in AI-powered testing
Module 6: Hands-on Labs and Capstone Project
- Lab 1: Automating test case generation via AI
- Lab 2: Developing a defect prediction model using historical test data
- Lab 3: Utilizing an LLM to review and optimize test scripts
- Capstone: End-to-end deployment of an AI-powered testing pipeline
Requirements
Candidates are anticipated to possess:
- At least 2 years of experience in software testing or QA positions
- Proficiency with test automation platforms (e.g., Selenium, JUnit, Cypress)
- Foundational programming skills (ideally in Python or JavaScript)
- Practical experience with version control and CI/CD systems (e.g., Git, Jenkins)
- While prior exposure to AI/ML is not mandatory, a sense of curiosity and an openness to experimentation 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.
