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Duration 21 hours
Course Outline
Foundations of AI in QA
- Defining Artificial Intelligence
- Distinguishing Machine Learning, Deep Learning, and Rule-based Systems
- The evolution of software testing through the lens of AI
- Primary advantages and obstacles of implementing AI in QA
Data and ML Fundamentals for Testers
- Differentiating between structured and unstructured data
- Comprehending features, labels, and training datasets
- Supervised versus unsupervised learning approaches
- Overview of model assessment metrics (accuracy, precision, recall, etc.)
- Application of real-world QA datasets
AI Applications in QA
- Generating test cases using AI
- Predicting defects via Machine Learning
- Optimizing test prioritization and risk-based strategies
- Implementing visual testing through computer vision
- Analyzing logs and detecting anomalies
- Applying Natural Language Processing (NLP) to test scripts
AI Toolkits for QA
- Survey of AI-capable QA platforms
- Leveraging open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototyping
- Introduction to Large Language Models (LLMs) in test automation
- Constructing a basic AI model to anticipate test failures
Embedding AI in QA Workflows
- Assessing the AI-readiness of your QA procedures
- Integrating AI with Continuous Integration: embedding intelligence into CI/CD pipelines
- Crafting intelligent test suites
- Oversight of AI model drift and retraining schedules
- Ethical implications of AI-driven testing
Practical Labs and Capstone Project
- Lab 1: Automating test case generation with AI
- Lab 2: Creating a defect prediction model using past test data
- Lab 3: Utilizing an LLM to audit and refine test scripts
- Capstone: Comprehensive implementation of an AI-driven testing pipeline
Requirements
Participants are anticipated to possess:
- Minimum of two years of experience in software testing or QA positions
- Knowledge of test automation frameworks (such as Selenium, JUnit, or Cypress)
- Foundational programming proficiency, ideally in Python or JavaScript
- Proficiency with version control and CI/CD utilities (for example, Git or Jenkins)
- Previous AI/ML experience is not mandatory; however, curiosity and a readiness to experiment are key
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
The instructor's teaching style was very good.
