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.
Testimonials (4)
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.
Kubra
Course - Automation Testing using 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.
