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Duration 21 hours
Course Outline
Introduction to AI for QA
- The fundamentals of Artificial Intelligence
- Distinguishing Machine Learning, Deep Learning, and Rule-based Systems
- The progression of software testing through the lens of AI
- Major advantages and potential challenges of AI in the QA domain
Data and ML Basics for Testers
- Comprehending the difference between structured and unstructured data
- The roles of features, labels, and training datasets
- Concepts of supervised and unsupervised learning
- An introduction to model evaluation metrics, including accuracy, precision, and recall
- Application of real-world QA datasets
AI Use Cases in QA
- Generating test cases using AI capabilities
- Predicting defects through Machine Learning
- Optimizing test prioritization and risk-based testing
- Performing visual testing with computer vision
- Analyzing logs and detecting anomalies
- Applying Natural Language Processing (NLP) to test scripts
AI Tools for QA
- An overview of QA platforms enhanced with AI
- Utilizing open-source libraries like Python, Scikit-learn, TensorFlow, and Keras for QA prototypes
- Introducing Large Language Models (LLMs) in test automation
- Developing a basic AI model to forecast test failures
Integrating AI into QA Workflows
- Assessing the AI-readiness of existing QA processes
- Aligning continuous integration with AI: embedding intelligence into CI/CD pipelines
- Architecting intelligent test suites
- Oversight of AI model drift and management of retraining cycles
- Ethical implications and considerations in 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: Leveraging an LLM to review and refine test scripts
- Capstone: Executing an end-to-end AI-powered testing pipeline implementation
Requirements
Learners should bring the following:
- At least two years of experience in software testing or QA roles
- Working knowledge of test automation tools such as Selenium, JUnit, and Cypress
- Basic programming skills, preferably in Python or JavaScript
- Hands-on experience with version control and CI/CD systems like Git and Jenkins
- While no previous AI/ML background is mandatory, a keen curiosity and eagerness to experiment are highly desirable
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.
