Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Module 1: Introduction to AI for QA
- Defining Artificial Intelligence.
- Comparing Machine Learning, Deep Learning, and Rule-based Systems.
- The progression of software testing through AI integration.
- Primary advantages and challenges of AI in QA.
Module 2: Data and ML Basics for Testers
- Distinguishing between structured and unstructured data.
- Concepts of features, labels, and training datasets.
- Overview of supervised and unsupervised learning.
- Introduction to model evaluation metrics (accuracy, precision, recall, etc.).
- Examination of real-world QA datasets.
Module 3: AI Use Cases in QA
- AI-driven test case generation.
- Defect prediction utilizing ML.
- Test prioritization and risk-based testing strategies.
- Visual testing via computer vision.
- Log analysis and anomaly detection techniques.
- Utilizing Natural Language Processing (NLP) for test scripts.
Module 4: AI Tools for QA
- Survey of AI-enabled QA platforms.
- Utilizing open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototypes.
- Introduction to LLMs in test automation.
- Developing a basic AI model to predict test failures.
Module 5: Integrating AI into QA Workflows
- Assessing the AI readiness of QA processes.
- Continuous integration and AI: Embedding intelligence into CI/CD pipelines.
- Designing intelligent test suites.
- Managing AI model drift and retraining cycles.
- Ethical considerations in AI-powered testing.
Module 6: Hands-on Labs and Capstone Project
- Lab 1: Automating test case generation using AI.
- Lab 2: Constructing a defect prediction model using historical test data.
- Lab 3: Employing an LLM to review and optimize test scripts.
- Capstone: Implementing an end-to-end AI-powered testing pipeline.
Requirements
Participants are expected to possess:
- Over two years of experience in software testing or QA positions.
- Familiarity with test automation platforms (e.g., Selenium, JUnit, Cypress).
- Basic proficiency in programming (ideally in Python or JavaScript).
- Experience with version control and CI/CD tools (e.g., Git, Jenkins).
- No previous AI/ML experience is necessary, though a curious mindset and willingness to experiment are crucial.
21 Hours
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.
