AI+ Quality Assurance (AT- 920 Self-Paced Training)
Ziele der Schulung
The AI+ Quality Assurance certification is designed to equip learners with the skills to integrate artificial intelligence into quality assurance processes, automating testing tasks, predicting defects, and optimizing testing performance. It helps enhance testing efficiency and accuracy using AI tools, supports career advancement by teaching in-demand QA skills, and provides real-world application experience. What Will You Learn? You’ll gain knowledge in QA fundamentals, manual and automation testing techniques, performance testing tools, AI-driven testing strategies, and practical hands-on projects.
Zielgruppe Seminar
- QA Professionals looking to enhance testing with AI-driven tools
- Software Testers aiming to improve defect detection and automation
- Developers interested in integrating AI into software testing cycles
- Data Scientists applying AI/ML to QA problems
- Tech Managers leading QA and testing teams with advanced technologies
Voraussetzungen
- Programming Skills: Basic knowledge of Python and familiarity with software testing lifecycle and tools
- Basics of QA: Basic knowledge of Quality Assurance principles and practices
- Basics of AI: Foundational knowledge of machine learning concepts is beneficial but not mandatory
Seminarinhalt
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
Hinweise
Prüfung und Zertifizierung
- Exam: 50 MCQs, 90 Minutes
- Passing Score: 70% (35/50)
- Modules: 10, followed by single exam session
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