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Duration 21 hours
Course Outline
Introduction to AI in QA
- Defining Artificial Intelligence
- Differentiating Machine Learning, Deep Learning, and Rule-based Systems
- The progression of software testing through AI
- Primary advantages and challenges of adopting AI in QA
Foundations of Data and ML for Testers
- Distinguishing between structured and unstructured data
- Understanding features, labels, and training datasets
- Concepts of supervised and unsupervised learning
- Introduction to model evaluation metrics (accuracy, precision, recall, etc.)
- Examination of real-world QA datasets
Practical AI Applications in QA
- Generating test cases with AI assistance
- Predicting defects using ML algorithms
- Optimizing test prioritization and risk-based strategies
- Implementing visual testing via computer vision
- Analyzing logs and detecting anomalies
- Applying Natural Language Processing (NLP) to test scripts
AI Tools for QA Professionals
- Survey of AI-enabled QA platforms
- Utilizing open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototyping
- Introduction to LLMs in the context of test automation
- Developing a basic AI model to forecast test failures
Integrating AI into QA Workflows
- Assessing the AI-readiness of existing QA processes
- Combining continuous integration with AI: embedding intelligence into CI/CD pipelines
- Architecting 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: Creating a defect prediction model from historical test data
- Lab 3: Leveraging an LLM to review and refine test scripts
- Capstone: Deploying a complete, end-to-end AI-powered testing pipeline
Requirements
Participants should possess the following background:
- At least 2 years of experience in software testing or QA positions
- Proficiency with test automation frameworks (such as Selenium, JUnit, or Cypress)
- Fundamental programming knowledge (ideally in Python or JavaScript)
- Hands-on experience with version control and CI/CD tools (like Git and Jenkins)
- No prior AI/ML background is necessary, although a strong curiosity and readiness to experiment are highly valued
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.
