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Duration 21 hours
Course Outline
Introduction to AI for QA
- The fundamentals of Artificial Intelligence.
- Distinguishing between Machine Learning, Deep Learning, and Rule-based Systems.
- The evolution of software testing through AI integration.
- Key advantages and challenges of AI in QA environments.
Data and ML Basics for Testers
- Understanding the difference 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.).
- Exploring real-world QA datasets.
AI Use Cases in QA
- Generating test cases using AI.
- Predicting defects with Machine Learning.
- Test prioritization and risk-based testing strategies.
- Visual testing utilizing computer vision.
- Analyzing logs and detecting anomalies.
- Applying Natural Language Processing (NLP) to test scripts.
AI Tools for QA
- Overview of AI-enabled QA platforms.
- Utilizing open-source libraries (such as Python, Scikit-learn, TensorFlow, Keras) for QA prototypes.
- Introduction to Large Language Models (LLMs) in test automation.
- Developing a basic AI model to predict test failures.
Integrating AI into QA Workflows
- Assessing the AI-readiness of existing QA processes.
- Combining Continuous Integration with AI: embedding intelligence into CI/CD pipelines.
- Designing intelligent test suites.
- Managing AI model drift and retraining cycles.
- Ethical considerations in AI-powered testing.
Hands-on Labs and Capstone Project
- Lab 1: Automating test case generation with AI.
- Lab 2: Creating a defect prediction model using historical test data.
- Lab 3: Leveraging an LLM to review and optimize test scripts.
- Capstone: End-to-end implementation of an AI-powered testing pipeline.
Requirements
Participants are expected to possess the following background:
- A minimum of two years of experience in software testing or QA roles.
- Proficiency with test automation tools such as Selenium, JUnit, or Cypress.
- Fundamental programming knowledge, ideally in Python or JavaScript.
- Experience utilizing version control and CI/CD systems like Git and Jenkins.
- No previous AI/ML experience is necessary, but a strong curiosity and readiness to experiment are highly valued.
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
Key topics can be discussed and agreed upon with the trainer in advance. Relaxed and pleasant atmosphere during the seminar days.
