План на курса

Module 1: Introduction to AI for QA

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

Module 2: Data and ML Basics for Testers

  • Understanding structured vs unstructured data
  • Features, labels, and training datasets
  • Supervised and unsupervised learning
  • Intro to model evaluation (accuracy, precision, recall, etc.)
  • Real-world QA datasets

Module 3: AI Use Cases in QA

  • AI-powered test case generation
  • Defect prediction using ML
  • Test prioritization and risk-based testing
  • Visual testing with computer vision
  • Log analysis and anomaly detection
  • Natural language processing (NLP) for test scripts

Module 4: 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

Module 5: Integrating AI into QA Workflows

  • Evaluating AI-readiness of your QA processes
  • Continuous integration and AI: how to embed intelligence into CI/CD pipelines
  • Designing intelligent test suites
  • Managing AI model drift and retraining cycles
  • Ethical considerations in AI-powered testing

Module 6: Hands-on Labs and Capstone Project

  • Lab 1: Automate test case generation using AI
  • Lab 2: Build a defect prediction model using historical test data
  • Lab 3: Use an LLM to review and optimize test scripts
  • Capstone: End-to-end implementation of an AI-powered testing pipeline

Изисквания

Участващите се очаква да разполагат с:

  • 2+ години опит в роли за тестване на софтуер/QA
  • Знаемост с инструменти за автоматизиране на тестове (напр., Selenium, JUnit, Cypress)
  • Основни познания в програмиране (предпочитано на Python или JavaScript)
  • Опит с контрол на версии и инструменти за CI/CD (напр., Git, Jenkins)
  • Няма необходимо предишен опит в AI/ML, но любопитство и готовност да експериментира са необходими
 21 Часа

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