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 Duration 21 hours

Course Outline

Introduction to AI for QA

  • What is Artificial Intelligence?
  • Machine Learning vs. Deep Learning vs. Rule-based Systems
  • The evolution of software testing driven by AI
  • Key benefits and challenges of AI in QA

Data and ML Basics for Testers

  • Distinguishing between structured and unstructured data
  • Understanding features, labels, and training datasets
  • Supervised and unsupervised learning approaches
  • Introduction to model evaluation metrics (accuracy, precision, recall, etc.)
  • Real-world QA dataset examples

AI Use Cases in QA

  • Generating test cases with AI
  • Predicting defects using ML
  • Test prioritization and risk-based testing strategies
  • Visual testing via computer vision
  • Log analysis and anomaly detection
  • Using Natural Language Processing (NLP) for test scripts

AI Tools for QA

  • Overview of AI-enabled QA platforms
  • Using open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototypes
  • Introduction to LLMs in test automation
  • Building a simple AI model to predict test failures

Integrating AI into QA Workflows

  • Evaluating the AI-readiness of your 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

Hands-on Labs and Capstone Project

  • Lab 1: Automating test case generation using AI
  • Lab 2: Building a defect prediction model using historical test data
  • Lab 3: Utilizing 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:

  • Two or more years of experience in software testing or QA roles
  • Familiarity with test automation tools (e.g., Selenium, JUnit, Cypress)
  • Basic programming knowledge, preferably in Python or JavaScript
  • Experience with version control and CI/CD tools (e.g., Git, Jenkins)
  • No prior AI/ML experience is required, although a strong curiosity and willingness to experiment are essential

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