MasteringAI in Software Testing & QA: A 2023 Comprehensive Guide
Master AI in QA: From Automated Test Cases to Predictive Analytics, Performance Testing & Ethical AI Applications
Updated on Oct, 2025
Language - English
Development ,Data Science,Generative AI
Lectures -100
Resources -1
Duration -5.5 hours
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Course Description
1. Define AI's role in software testing.
2. Describe the evolution from manual to AI-driven testing.
3. Explain basic AI principles in testing applications.
4. Identify core technologies in AI software testing.
5. Discuss AI capabilities and limitations in testing.
6. Outline the pillars of quality assurance in software development.
7. Describe the importance of automated testing.
8. Explain machine learning's role in QA.
9. Identify tools for modern QA professionals.
10. Differentiate between AI-enabled and traditional QA.
11. Generate test cases using AI principles.
12. Improve test coverage with AI-based cases.
13. Analyze effectiveness of AI-generated test scenarios.
14. Identify challenges in AI test case generation.
15. Use AI to detect software defects efficiently.
16. Integrate AI in bug tracking and management.
17. Apply predictive analytics in software testing.
18. Assess quality standards in development with AI.
19. Utilize AI for performance test script creation.
20. Enhance user experience quality with AI techniques.
2. Describe the evolution from manual to AI-driven testing.
3. Explain basic AI principles in testing applications.
4. Identify core technologies in AI software testing.
5. Discuss AI capabilities and limitations in testing.
6. Outline the pillars of quality assurance in software development.
7. Describe the importance of automated testing.
8. Explain machine learning's role in QA.
9. Identify tools for modern QA professionals.
10. Differentiate between AI-enabled and traditional QA.
11. Generate test cases using AI principles.
12. Improve test coverage with AI-based cases.
13. Analyze effectiveness of AI-generated test scenarios.
14. Identify challenges in AI test case generation.
15. Use AI to detect software defects efficiently.
16. Integrate AI in bug tracking and management.
17. Apply predictive analytics in software testing.
18. Assess quality standards in development with AI.
19. Utilize AI for performance test script creation.
20. Enhance user experience quality with AI techniques.
2. Basic understanding of software development processes and methodologies.
3. Familiarity with the concepts of software testing and quality assurance.
4. An introductory knowledge of artificial intelligence and machine learning principles.
2. Describe the evolution from manual to AI-driven testing.
3. Explain basic AI principles in testing applications.
4. Identify core technologies in AI software testing.
5. Discuss AI capabilities and limitations in testing.
6. Outline the pillars of quality assurance in software development.
7. Describe the importance of automated testing.
8. Explain machine learning's role in QA.
9. Identify tools for modern QA professionals.
10. Differentiate between AI-enabled and traditional QA.
11. Generate test cases using AI principles.
12. Improve test coverage with AI-based cases.
13. Analyze effectiveness of AI-generated test scenarios.
14. Identify challenges in AI test case generation.
15. Use AI to detect software defects efficiently.
16. Integrate AI in bug tracking and management.
17. Apply predictive analytics in software testing.
18. Assess quality standards in development with AI.
19. Utilize AI for performance test script creation.
20. Enhance user experience quality with AI techniques.
Goals
1. Define AI's role in software testing.2. Describe the evolution from manual to AI-driven testing.
3. Explain basic AI principles in testing applications.
4. Identify core technologies in AI software testing.
5. Discuss AI capabilities and limitations in testing.
6. Outline the pillars of quality assurance in software development.
7. Describe the importance of automated testing.
8. Explain machine learning's role in QA.
9. Identify tools for modern QA professionals.
10. Differentiate between AI-enabled and traditional QA.
11. Generate test cases using AI principles.
12. Improve test coverage with AI-based cases.
13. Analyze effectiveness of AI-generated test scenarios.
14. Identify challenges in AI test case generation.
15. Use AI to detect software defects efficiently.
16. Integrate AI in bug tracking and management.
17. Apply predictive analytics in software testing.
18. Assess quality standards in development with AI.
19. Utilize AI for performance test script creation.
20. Enhance user experience quality with AI techniques.
Prerequisites
1. There are no requirements or pre-requisites for this course, but the items listed below are a guide to useful background knowledge which will increase the value and benefits of this course.2. Basic understanding of software development processes and methodologies.
3. Familiarity with the concepts of software testing and quality assurance.
4. An introductory knowledge of artificial intelligence and machine learning principles.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction to AI in Software Testing
5 Lectures
-
Understanding Artificial Intelligence in Quality Assurance
03:25
03:25
-
Evolution of Software Testing: From Manual to AI-driven
02:59
02:59
-
Basic Principles of AI in Testing Applications
03:25
03:25
-
Core Technologies Powering AI in Software Testing
03:36
03:36
-
Exploring the Capabilities and Limitations of AI in Testing
03:24
03:24
Fundamentals of Software Quality Assurance
5 Lectures
AI-driven Test Case Generation
5 Lectures
AI in Defect Detection and Management
5 Lectures
Predictive Analytics in Software Testing
5 Lectures
AI and Quality Standards in Software Development
5 Lectures
AI in Performance and Load Testing
5 Lectures
AI for Ensuring User Experience Quality
4 Lectures
Security Testing in the Age of AI
5 Lectures
Ethics and Bias in AI Testing Tools
5 Lectures
AI in Risk Management and QA
5 Lectures
Automating QA Processes with AI
5 Lectures
AI-driven Insights for Quality Improvement
5 Lectures
Enhancing Customer Satisfaction with AI QA
5 Lectures
Continual Learning and Improvement with AI in QA
5 Lectures
Integration Challenges of AI in Existing QA Processes
5 Lectures
AI in Mobile Application Testing
5 Lectures
The Role of AI in Agile and DevOps
5 Lectures
ROI Analysis of AI in Software Testing
5 Lectures
The Future of AI in Software Testing and QA
5 Lectures
Notes
1 Lectures
Instructor Details
Peter Alkema
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