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Course Outline

Module 1: Introduction to AI for QA

  • What constitutes Artificial Intelligence?
  • Distinguishing Machine Learning from Deep Learning and Rule-based Systems
  • The transformation of software testing through AI
  • Primary advantages and challenges of AI in QA

Module 2: Data and ML Basics for Testers

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

Module 3: AI Use Cases in QA

  • AI-driven 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
  • Utilizing open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototypes
  • Introduction to LLMs in test automation
  • Constructing a basic AI model to predict test failures

Module 5: Integrating AI into QA Workflows

  • Assessing 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

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

Requirements

Participants are expected to have:

  • Over 2 years of experience in software testing or QA positions
  • Familiarity with test automation frameworks (e.g., Selenium, JUnit, Cypress)
  • Foundational knowledge of programming (ideally in Python or JavaScript)
  • Experience with version control and CI/CD tools (e.g., Git, Jenkins)
  • No previous AI/ML background is necessary, although curiosity and a willingness to experiment are vital
 21 Hours

Custom Corporate Training

Training solutions designed exclusively for businesses.

  • Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
  • Flexible Schedule: Dates and times adapted to your team's agenda.
  • Format: Online (live), In-company (at your offices), or Hybrid.
Investment

Price per private group, online live training, starting from 4800 € + VAT*

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