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Duration 21 hours
Course Outline
Introduction to AI for QA
- Fundamentals of Artificial Intelligence
- Distinguishing Machine Learning, Deep Learning, and Rule-Based Systems
- The Trajectory of Software Testing in the AI Era
- Core Benefits and Challenges of Integrating AI into QA
Data and ML Fundamentals for Testers
- Differentiating between structured and unstructured data
- Understanding features, labels, and training datasets
- Concepts of Supervised and Unsupervised Learning
- Basics of model evaluation metrics (accuracy, precision, recall, etc.)
- Analysis of real-world QA datasets
AI Applications in QA
- Generating test cases with AI
- Predicting defects using ML algorithms
- Optimising test prioritisation and risk-based testing
- Visual testing leveraging computer vision
- Analysing logs and detecting anomalies
- Applying Natural Language Processing (NLP) to test scripts
AI Tools for QA
- Survey of AI-enabled QA platforms
- Leveraging open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototyping
- Role of LLMs in test automation
- Developing a basic AI model for test failure prediction
Integrating AI into QA Workflows
- Assessing the AI-readiness of existing QA processes
- Aligning Continuous Integration with AI: embedding intelligence into CI/CD pipelines
- Architecture of intelligent test suites
- Oversight of AI model drift and retraining cycles
- Ethical implications of AI-powered testing
Practical Labs and Capstone Project
- Lab 1: Automating test case generation with AI
- Lab 2: Constructing a defect prediction model using historical test data
- Lab 3: Utilising an LLM to review and refine test scripts
- Capstone: End-to-end deployment of an AI-driven testing pipeline
Requirements
Participants are expected to bring the following:
- Over two years of professional experience in software testing or QA roles
- Proficiency with test automation frameworks (e.g., Selenium, JUnit, Cypress)
- Foundational programming skills (ideally in Python or JavaScript)
- Hands-on experience with version control and CI/CD systems (e.g., Git, Jenkins)
- No prior AI/ML background is necessary, although curiosity and a willingness to experiment are key
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
Key topics can be discussed and agreed upon with the trainer in advance. Relaxed and pleasant atmosphere during the seminar days.
