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Duration 21 hours
Course Outline
Foundations of AI in QA
- Defining Artificial Intelligence
- Comparing Machine Learning, Deep Learning, and Rule-based Systems
- The trajectory of software testing through the lens of AI
- Primary advantages and obstacles of AI in QA
Data and ML Fundamentals for Testers
- Distinguishing between structured and unstructured data
- Understanding features, labels, and training datasets
- Concepts of supervised and unsupervised learning
- Basics of model evaluation (including accuracy, precision, and recall)
- Application of real-world QA datasets
AI Applications in QA
- Generating test cases with AI assistance
- Predicting defects through Machine Learning
- Strategic test prioritization and risk-based approaches
- Implementing visual testing via computer vision
- Analyzing logs and detecting anomalies
- Leveraging Natural Language Processing (NLP) for test scripting
AI Tooling for QA
- Survey of AI-integrated QA platforms
- Utilizing open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototyping
- Overview of Large Language Models (LLMs) in test automation
- Constructing a basic AI model for test failure prediction
Integrating AI into QA Workflows
- Assessing the AI-readiness of existing QA processes
- Continuous integration and AI: embedding intelligence into CI/CD pipelines
- Architecting intelligent test suites
- Oversight of AI model drift and retraining schedules
- Ethical implications of AI-driven testing
Practical Labs and Capstone Project
- Lab 1: Automating test case generation via AI
- Lab 2: Developing a defect prediction model from historical test data
- Lab 3: Employing an LLM to review and refine test scripts
- Capstone: Comprehensive implementation of an AI-driven testing pipeline
Requirements
Attendees should possess the following qualifications:
- Over two years of experience in software testing or QA positions
- Proficiency with test automation frameworks (e.g., Selenium, JUnit, Cypress)
- Foundational programming knowledge (Python or JavaScript preferred)
- Experience utilizing version control and CI/CD systems (e.g., Git, Jenkins)
- No previous AI/ML background is necessary, but a curious mindset and willingness to experiment are crucial
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.
