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Duration 21 hours
Course Outline
Foundations of AI in QA
- Defining Artificial Intelligence
- Distinguishing between Machine Learning, Deep Learning, and Rule-based Systems
- The progression of software testing through the lens of AI
- Primary advantages and potential hurdles of AI in QA
Essentials of Data and ML for Testers
- Differentiating between structured and unstructured data
- Comprehending features, labels, and training datasets
- Contrasting supervised and unsupervised learning paradigms
- Overview of model assessment metrics (accuracy, precision, recall, etc.)
- Application of real-world QA datasets
Practical AI Applications in QA
- Generating test cases using AI
- Predicting defects via Machine Learning
- Optimizing test prioritization and risk-based testing
- Implementing visual testing through computer vision
- Analyzing logs and detecting anomalies
- Utilizing Natural Language Processing (NLP) for test scripting
AI-Enabled QA Toolkits
- Survey of AI-integrated QA platforms
- Utilizing open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) to build QA prototypes
- Introduction to Large Language Models (LLMs) in test automation
- Constructing a basic AI model to forecast test failures
Embedding AI into QA Processes
- Assessing the AI-readiness of current QA procedures
- Aligning Continuous Integration with AI: embedding intelligence into CI/CD pipelines
- Architecting intelligent test suites
- Addressing AI model drift and managing retraining cycles
- Navigating ethical considerations in AI-driven testing
Practical Labs and Capstone Project
- Lab 1: Automating test case creation with AI
- Lab 2: Developing a defect prediction model from historical test data
- Lab 3: Leveraging an LLM to audit and optimize test scripts
- Capstone: Executing a complete AI-powered testing pipeline
Requirements
Candidates are expected to possess:
- A minimum of two years of professional experience in software testing or QA positions
- Working knowledge of test automation frameworks (such as Selenium, JUnit, or Cypress)
- Fundamental programming skills, preferably in Python or JavaScript
- Hands-on experience with version control and CI/CD systems (e.g., Git, Jenkins)
- No prior background in AI/ML is necessary, although a mindset oriented toward experimentation and curiosity is highly recommended
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.
