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Duration 21 hours
Course Outline
Introduction to AI for QA
- The definition and scope of Artificial Intelligence
- Distinguishing between Machine Learning, Deep Learning, and Rule-based Systems
- The progression of software testing through AI adoption
- Primary advantages and potential challenges of AI in QA
Data and ML Basics for Testers
- Differentiating between structured and unstructured data
- Understanding features, labels, and training datasets
- Concepts of supervised and unsupervised learning
- Introduction to model evaluation metrics (accuracy, precision, recall, etc.)
- Analysis of real-world QA datasets
AI Use Cases in QA
- Generating test cases using AI power
- Predicting defects with ML techniques
- Optimizing test prioritization and risk-based testing
- Implementing visual testing via computer vision
- Performing log analysis and anomaly detection
- Applying Natural Language Processing (NLP) for test scripts
AI Tools for QA
- An overview of AI-enabled QA platforms
- Utilizing open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototypes
- Introducing LLMs in the context of test automation
- Constructing a basic AI model to forecast test failures
Integrating AI into QA Workflows
- Assessing the AI-readiness of your QA processes
- Continuous integration and AI: embedding intelligence into CI/CD pipelines
- Architecting intelligent test suites
- Managing AI model drift and retraining cycles
- Ethical implications of AI-powered testing
Hands-on Labs and Capstone Project
- Lab 1: Automating test case generation with AI
- Lab 2: Developing 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-driven testing pipeline
Requirements
Candidates are expected to possess the following:
- At least two years of experience in software testing or QA roles
- Proficiency with test automation tools (such as Selenium, JUnit, or Cypress)
- A basic understanding of programming languages, preferably Python or JavaScript
- Practical experience with version control and CI/CD tools (e.g., Git, Jenkins)
- No prior AI/ML experience is mandatory, although curiosity and a readiness to experiment are crucial
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.
