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Duration 21 hours
Course Outline
Foundations of AI for QA
- Defining Artificial Intelligence
- Machine Learning vs. Deep Learning vs. Rule-Based Systems
- The transformation of software testing through AI
- Major advantages and obstacles of AI in the QA domain
Data and ML Fundamentals for Testers
- Distinguishing between structured and unstructured data
- Understanding features, labels, and training datasets
- Supervised versus unsupervised learning techniques
- Basics of model assessment (accuracy, precision, recall, and more)
- Practical QA dataset examples
AI Applications in QA
- Generating test cases with AI
- Predicting defects via Machine Learning
- Test prioritization and risk-based testing strategies
- Visual testing utilizing computer vision
- Analyzing logs and detecting anomalies
- Leveraging NLP for test script development
AI Tooling for QA
- Survey of AI-enabled QA platforms
- Using open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) to build QA prototypes
- Introduction to LLMs within test automation
- Developing a basic AI model for predicting test failures
Embedding AI in QA Workflows
- Assessing the AI-readiness of your QA processes
- Integrating AI with Continuous Integration: embedding intelligence into CI/CD pipelines
- Architecting intelligent test suites
- Overseeing AI model drift and retraining schedules
- Ethical implications of 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: Implementing a complete AI-powered testing pipeline end-to-end
Requirements
Candidates should possess the following:
- At least 2 years of experience in software testing or QA positions
- 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)
- While prior AI/ML experience is not mandatory, strong curiosity and a readiness to experiment are vital
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.
