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Duration 21 hours
Course Outline
Introduction to AI for QA
- Defining Artificial Intelligence
- Distinguishing between Machine Learning, Deep Learning, and Rule-based Systems
- The trajectory of software testing through the lens of AI
- Primary advantages and obstacles of implementing AI in QA
Data and ML Basics for Testers
- Differentiating between structured and unstructured data
- Understanding features, labels, and training datasets
- Supervised versus unsupervised learning paradigms
- Fundamentals of model assessment (accuracy, precision, recall, etc.)
- Examination of real-world QA datasets
AI Use Cases in QA
- Automated test case generation via AI
- Predicting defects using Machine Learning
- Strategies for test prioritisation and risk-based testing
- Visual testing applications using computer vision
- Log analysis and anomaly identification
- Applying Natural Language Processing (NLP) to test scripts
AI Tools for QA
- Survey of AI-enabled QA platforms
- Leveraging open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototyping
- Introduction to Large Language Models (LLMs) in test automation
- Developing a basic AI model for predicting test failures
Integrating AI into QA Workflows
- Assessing the AI-readiness of current 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
Hands-on 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: Utilising an LLM to review and optimise test scripts
- Capstone: End-to-end execution of an AI-powered testing pipeline
Requirements
Candidates are expected to have:
- At least two years of experience in software testing or QA positions.
- Proficiency with test automation frameworks (e.g., Selenium, JUnit, Cypress).
- Basic programming knowledge, preferably in Python or JavaScript.
- Hands-on experience with version control and CI/CD systems (e.g., Git, Jenkins).
- No prior AI/ML background is necessary, though a strong curiosity and openness to experimentation are essential.
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.
