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Duration 21 hours
Course Outline
Introduction to AI for QA
- The definition and scope of Artificial Intelligence
- Comparing Machine Learning, Deep Learning, and Rule-based Systems
- The evolution of software testing in the era of AI
- Primary advantages and potential challenges of AI in QA
Data and ML Fundamentals for Testers
- Distinguishing between structured and unstructured data
- Understanding features, labels, and training datasets
- Overview of supervised and unsupervised learning
- Basics of model evaluation metrics (accuracy, precision, recall, etc.)
- Exploration of real-world QA datasets
Practical AI Applications in QA
- Generating test cases using AI
- Predicting defects through ML algorithms
- Test prioritization and risk-based testing strategies
- Implementing visual testing with computer vision
- Analyzing logs and detecting anomalies
- Leveraging Natural Language Processing (NLP) for test scripts
Essential AI Tools for QA
- Survey of AI-enabled QA platforms
- Creating QA prototypes using open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras)
- Introduction to Large Language Models (LLMs) in test automation
- Developing a basic AI model to anticipate test failures
Integrating AI into QA Workflows
- Assessing the AI-readiness of existing QA processes
- Continuous integration and AI: Embedding intelligence into CI/CD pipelines
- Strategy design for intelligent test suites
- Managing AI model drift and retraining schedules
- Ethical considerations in AI-driven testing
Practical Labs and Capstone Project
- Lab 1: Automating test case generation with AI
- Lab 2: Constructing a defect prediction model from historical test data
- Lab 3: Utilizing an LLM to review and refine test scripts
- Capstone: End-to-end deployment of an AI-powered testing pipeline
Requirements
Participants should possess the following background:
- At least two years of professional experience in software testing or QA roles
- Proficiency with test automation frameworks (such as Selenium, JUnit, or Cypress)
- Foundational programming skills (Python or JavaScript preferred)
- Experience with version control and CI/CD systems (e.g., Git, Jenkins)
- No previous AI/ML experience is necessary, but a curiosity-driven mindset and willingness to experiment are crucial
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.
