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Course Outline
Foundations of AI in Quality Assurance
- Definition and scope of Artificial Intelligence
- Distinctions among Machine Learning, Deep Learning, and rule-based architectures
- Historical progression of AI in software testing methodologies
- Principal advantages and operational challenges of AI in QA environments
Data and Machine Learning Principles for Testers
- Differentiating structured and unstructured data types
- Roles of features, labels, and training datasets
- Supervised and unsupervised learning paradigms
- Introduction to model evaluation metrics, including accuracy, precision, and recall
- Application of real-world QA datasets for for government use cases
AI Applications in Quality Assurance
- Automated test case generation via AI
- Defect prediction utilizing machine learning models
- Risk-based test prioritization strategies
- Visual testing techniques leveraging computer vision
- System log analysis and anomaly identification
- Utilizing Natural Language Processing (NLP) for test script creation
AI Technologies for Quality Assurance
- Survey of AI-integrated QA platforms
- Leveraging open-source libraries, such as Python, Scikit-learn, TensorFlow, and Keras, for QA prototype development
- Foundations of Large Language Models (LLMs) in test automation
- Development of a basic AI model for test failure prediction for government applications
Incorporating AI into QA Workflows
- Assessing the AI readiness of current QA processes
- Continuous integration with AI: embedding intelligence into CI/CD pipelines
- Architecture of intelligent test suites
- Oversight of AI model drift and retraining cycles
- Ethical and compliance considerations in AI-driven testing for government sectors
Practical Exercises and Capstone Project
- Lab 1: Automating test case generation using AI
- Lab 2: Developing a defect prediction model from historical test data
- Lab 3: Utilizing an LLM to review and optimize test scripts
- Capstone: End-to-end implementation of an AI-powered testing pipeline
Requirements
Participants are expected to have:
- Two or more years of experience in software testing or QA roles
- Familiarity with test automation tools (e.g., Selenium, JUnit, Cypress)
- Basic programming knowledge, preferably in Python or JavaScript
- Experience with version control and CI/CD tools (e.g., Git, Jenkins)
- No prior AI/ML experience is required; however, a commitment to experimentation is essential for this curriculum for government personnel
21 Hours
Testimonials (3)
The possibilities of postman and future use of it.
Gordana Gacic - SEE Digital D.O.O.
Course - API Testing with Postman
hands on exercises, easier to retain information
ashley bolen - Insurance Corporation of British Columbia
Course - Test Automation with Selenium
The instructor's teaching style was very good.
