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Duration 21 hours
Course Outline
Introduction to AI for QA
- Defining Artificial Intelligence
- Distinguishing between Machine Learning, Deep Learning, and Rule-based Systems
- The evolution of software testing in the age of AI
- Key advantages and challenges of implementing AI in QA
Data and ML Basics for Testers
- Understanding the difference between structured and unstructured data
- Exploring features, labels, and training datasets
- Supervised versus unsupervised learning
- An introduction to model evaluation metrics (accuracy, precision, recall, etc.)
- Analysis of real-world QA datasets
AI Use Cases in QA
- AI-driven test case generation
- Predicting defects using ML
- Test prioritisation and risk-based testing strategies
- Visual testing leveraging computer vision
- Log analysis and anomaly detection
- Applying Natural Language Processing (NLP) to test scripts
AI Tools for QA
- Overview of AI-enabled QA platforms
- Utilising open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototyping
- Introduction to LLMs within test automation
- Developing a basic AI model for predicting test failures
Integrating AI into QA Workflows
- Assessing the AI-readiness of current QA processes
- Continuous integration and AI: embedding intelligence into CI/CD pipelines
- Designing intelligent test suites
- Managing AI model drift and retraining cycles
- Ethical considerations in AI-powered testing
Hands-on Labs and Capstone Project
- Lab 1: Automating test case generation with AI
- Lab 2: Building a defect prediction model using historical test data
- Lab 3: Leveraging an LLM to review and optimise test scripts
- Capstone: End-to-end implementation of an AI-powered testing pipeline
Requirements
Participants are expected to possess:
- A minimum of two years' experience in software testing or QA roles
- Proficiency with test automation tools (e.g., Selenium, JUnit, Cypress)
- Basic programming knowledge (preferably in Python or JavaScript)
- Practical experience with version control and CI/CD tools (e.g., Git, Jenkins)
- No prior AI/ML experience is required; however, curiosity and a willingness to experiment are essential
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.
