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Duration 21 hours
Course Outline
Introduction to AI for QA
- The definition of Artificial Intelligence
- Distinguishing between Machine Learning, Deep Learning, and Rule-based Systems
- The transformation of software testing through AI
- Primary benefits and challenges associated with AI in QA
Data and ML Basics for Testers
- Differentiating between structured and unstructured data
- Understanding features, labels, and training datasets
- Supervised versus unsupervised learning
- Introduction to model evaluation metrics (accuracy, precision, recall, etc.)
- Examining real-world QA datasets
AI Use Cases in QA
- Generating test cases using AI
- Forecasting defects via Machine Learning
- Test prioritisation and risk-based testing strategies
- Implementing visual testing with computer vision
- Analyzing logs and detecting anomalies
- Applying Natural Language Processing (NLP) to test scripts
AI Tools for QA
- Overview of AI-enabled QA platforms
- Leveraging open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototypes
- The role of LLMs in test automation
- Developing a basic AI model to forecast test failures
Integrating AI into QA Workflows
- Assessing the AI-readiness of current QA processes
- Combining Continuous Integration with 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 using AI
- Lab 2: Creating a defect prediction model using historical test data
- Lab 3: Utilising 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:
- At least two years of experience in software testing or QA roles
- Proficiency with test automation tools (e.g., Selenium, JUnit, Cypress)
- Fundamental programming knowledge, ideally in Python or JavaScript
- Practical experience with version control and CI/CD tools (e.g., Git, Jenkins)
- No prior AI/ML experience is necessary, although a strong 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
Key topics can be discussed and agreed upon with the trainer in advance. Relaxed and pleasant atmosphere during the seminar days.
