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Duration 21 hours
Course Outline
Foundations of AI in QA
- The definition and scope of Artificial Intelligence
- Distinguishing between Machine Learning, Deep Learning, and Rule-based Systems
- The progression of software testing through the integration of AI
- Primary advantages and potential challenges of AI within QA
Data and ML Essentials for Testers
- Differentiating between structured and unstructured data
- Understanding features, labels, and training datasets
- Overview of supervised and unsupervised learning methods
- Introduction to model assessment metrics (such as accuracy, precision, and recall)
- Analysis of real-world QA datasets
AI Applications in the QA Domain
- Generating test cases with the aid of AI
- Forecasting defects using machine learning techniques
- Test prioritization and risk-based testing strategies
- Implementing visual testing via computer vision
- Analyzing logs and detecting anomalies
- Applying Natural Language Processing (NLP) to test scripting
AI Instrumentation for QA
- A survey of AI-enabled QA platforms
- Utilizing open-source libraries (like Python, Scikit-learn, TensorFlow, and Keras) to develop QA prototypes
- Exploring the role of Large Language Models (LLMs) in test automation
- Creating a basic AI model to forecast test failures
Embedding AI into QA Operations
- Assessing the AI-readiness of current QA procedures
- Integrating intelligence into CI/CD pipelines via continuous integration
- Architecting intelligent test suites
- Overseeing AI model drift and retraining schedules
- Ethical implications of AI-driven testing
Practical Labs and Capstone Project
- Lab 1: Streamlining test case generation with AI
- Lab 2: Developing a defect prediction model based on historical test data
- Lab 3: Leveraging an LLM to audit and enhance test scripts
- Capstone: Full-scale implementation of an AI-driven testing pipeline
Requirements
Participants are expected to possess the following:
- A minimum of two years of experience in software testing or QA positions
- Proficiency with test automation frameworks (such as Selenium, JUnit, or Cypress)
- Foundational programming knowledge, ideally in Python or JavaScript
- Hands-on experience with version control and CI/CD systems (e.g., Git, Jenkins)
- No previous AI/ML background is necessary, but a strong curiosity and eagerness to experiment are essential
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.
