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AI+ Quality Assurance Practitioner™

Course code: NPAT920

Formerly known as AI+ Quality Assurance™

Master AI-Driven Quality Assurance: Elevate Your Testing Efficiency, Accuracy, and Scalability

  • AI testing Mastery: Build practical expertise in AI-driven testing methodologies through hands-on engagement with real-world tools and scenarios.
  • Intelligent Automation Edge: Leverage intelligent automation techniques to sharpen defect identification and elevate performance testing workflows.
  • QA Career Fast-Track: Advance your QA career through a comprehensive, industry-aligned exam bundle, structured to keep you ahead in a rapidly evolving landscape.
75 EUR

91 EUR including VAT

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Course dates

Starting date: Upon request

Guaranteed

Type: Self-paced

Course duration: 40 hours

Language: en

Price without VAT: 285 EUR

Register

Starting date: Upon request

Guaranteed

Type: Self-paced

Course duration: 40 hours

Language: en

Price without VAT: 75 EUR

Register

Starting
date		 Place
Type Course
duration		 Language Price without VAT
G Upon request Self-paced 40 hours en 285 EUR Register
G Upon request Self-paced 40 hours en 75 EUR Register
G Guaranteed course

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Course description

QA Fundamentals

Understand the core principles of Quality Assurance (QA), including testing methodologies, tools, and processes to ensure software quality.

Manual Testing

Master manual testing techniques, including test case creation, test execution, and defect reporting to ensure software functionality meets requirements.

Automation Testing

Learn automation testing using popular tools like Selenium, Appium, and TestNG, and understand how automation enhances testing efficiency and accuracy.

Performance Testing

Gain expertise in performance testing tools like JMeter and LoadRunner, and learn how to evaluate software performance under different conditions.

Course structure

Module 1: Introduction to Quality Assurance (QA) and AI

  • 1.1 Overview of QA
  • 1.2 Introduction to AI in QA
  • 1.3 QA Metrics and KPIs
  • 1.4 Use of Data in QA

Module 2: Fundamentals of AI, ML, and Deep Learning

  • 2.1 AI Fundamentals
  • 2.2 Machine Learning Basics
  • 2.3 Deep Learning Overview
  • 2.4 Introduction to Large Language Models (LLMs)

Module 3: Test Automation with AI

  • 3.1 Test Automation Basics
  • 3.2 AI-Driven Test Case Generation
  • 3.3 Tools for AI Test Automation
  • 3.4 Integration into CI/CD Pipelines

Module 4: AI for Defect Prediction and Prevention

  • 4.1 Defect Prediction Techniques
  • 4.2 Preventive QA Practices
  • 4.3 AI for Risk-Based Testing
  • 4.4 Case Study: Defect Reduction with AI

Module 5: NLP for QA

  • 5.1 Basics of NLP
  • 5.2 NLP in QA
  • 5.3 LLMs for QA
  • 5.4 Case Study: Using NLP for Bug Triaging

Module 6: AI for Performance Testing

  • 6.1 Performance Testing Basics
  • 6.2 AI in Performance Testing
  • 6.3 Visualization of Performance Metrics
  • 6.4 Case Study: AI in Performance Testing of a Cloud App

Module 7: AI in Exploratory and Security Testing

  • 7.1 Exploratory Testing with AI
  • 7.2 AI in Security Testing
  • 7.3 Case Study: Enhancing Security Testing with AI

Module 8: Continuous Testing with AI

  • 8.1 Continuous Testing Overview
  • 8.2 AI for Regression Testing
  • 8.3 Use-Case: Risk-Based Continuous Testing

Module 9: Advanced QA Techniques with AI

  • 9.1 AI for Predictive Analytics in QA
  • 9.2 AI for Edge Cases
  • 9.3 Future Trends in AI + QA

Module 10: Capstone Project

Prerequisites

  • Programing Foundation: Familiarity with Python basics, along with a working understanding of the software testing lifecycle and commonly used testing tools.
  • QA Fundamentals: A foundational grasp of Quality Assurance principles and standard practices.
  • AI Awareness: Some exposure to core machine learning concepts is advantageous, though not a strict requirement.

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