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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.
- Main advantages and hurdles of AI in QA.
Module 2: Data and ML Basics for Testers
- Differentiating between structured and unstructured data.
- Concepts of features, labels, and training datasets.
- Supervised and unsupervised learning methods.
- Introduction to model evaluation metrics (accuracy, precision, recall, etc.).
- Practical QA datasets.
Module 3: AI Use Cases in QA
- AI-driven test case generation.
- Defect forecasting using ML.
- Test prioritization and risk-based testing.
- Visual testing via computer vision.
- Log analysis and anomaly detection.
- Natural language processing (NLP) for test script creation.
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: Utilizing an LLM to review and optimize test scripts.
- Capstone: Full-scale implementation of an AI-powered testing pipeline.
Requirements
Participants are anticipated to possess:
- At least 2 years of professional experience in software testing or QA positions.
- Proficiency with test automation tools (e.g., Selenium, JUnit, Cypress).
- Fundamental programming knowledge (preferably in Python or JavaScript).
- Experience using version control and CI/CD platforms (e.g., Git, Jenkins).
- No previous AI/ML background is necessary, though curiosity and a willingness to experiment are vital.
21 Hours
Testimonials (3)
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.
Lorenz - Continentale Lebensversicherung AG
Course - Advanced Selenium
I gained new knowledge and I'm pretty confident about it. Nothing unclear.
