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Duration 21 hours
Course Outline
AI Foundations for QA
- Defining Artificial Intelligence
- Distinguishing between Machine Learning, Deep Learning, and Rule-based Systems
- The progression of software testing in the age of AI
- Major advantages and challenges of implementing AI in QA
Essentials of Data and ML for Testers
- Differentiating 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.)
- Application of real-world QA datasets
Practical AI Applications in QA
- Generating test cases with AI
- Predicting defects using ML techniques
- Optimizing test prioritization and risk-based testing
- Implementing visual testing via computer vision
- Analyzing logs and detecting anomalies
- Leveraging NLP for test scripting
AI Toolsets for QA
- Survey of AI-enabled QA platforms
- Utilizing open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototypes
- Introduction to Large Language Models (LLMs) in test automation
- Developing a basic AI model for predicting test failures
Embedding AI into QA Workflows
- Assessing the AI-readiness of current QA processes
- Integrating AI with Continuous Integration: embedding intelligence into CI/CD pipelines
- Crafting intelligent test suites
- Oversight of AI model drift and retraining schedules
- Ethical perspectives on AI-driven testing
Practical Labs and Capstone Project
- Lab 1: Automating test case generation with AI
- Lab 2: Constructing a defect prediction model using historical test data
- Lab 3: Employing an LLM to review and refine test scripts
- Capstone: Comprehensive implementation of an AI-powered testing pipeline
Requirements
Participants should possess the following qualifications:
- At least two years of experience in software testing or QA positions
- Proficiency with test automation frameworks (such as Selenium, JUnit, or Cypress)
- Familiarity with programming basics (Python or JavaScript preferred)
- Working knowledge of version control and CI/CD systems (e.g., Git, Jenkins)
- No previous AI/ML background is necessary; however, a strong curiosity and eagerness to experiment are required
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.
