Driving Scalability and Accuracy in QA with AI and ML

Overview

Explore how AI and ML technologies are revolutionizing software testing, from automated test case generation to predictive analysis, addressing traditional QA challenges and enhancing scalability and accuracy.

Syllabus

• Introduction to AI and ML in Software Testing
Overview of AI and ML technologies
Transition from traditional QA to AI-driven QA
Key benefits of AI and ML in software testing
• Automated Test Case Generation
Understanding automated test case generation
AI tools for automated testing
Case studies of successful implementations
• Machine Learning for Test Prediction
Predictive analysis in QA
Algorithms and models used in test prediction
Examples of predictive testing improving QA processes
• Enhancing Scalability in QA with AI
Challenges in scaling traditional QA processes
AI strategies for scalable test processes
Tools and frameworks that enable QA scalability
• Improving Accuracy with AI-Driven Testing
Reducing false positives and negatives in testing
AI methods to enhance test accuracy
Real-world applications in critical systems
• Addressing Traditional QA Challenges
Cost and resource optimization in QA
Reducing time-to-market with AI-driven methods
Managing risks and biases in AI testing
• Tools and Frameworks for AI and ML in QA
Review of popular tools and platforms
Hands-on exercises with select tools
Comparative analysis of available solutions
• Case Studies and Industry Applications
Examination of AI and ML adoption in various industries
Lessons learned and best practices
Future trends and the evolving landscape of QA
• Conclusion and Future Directions
Recap of key concepts learned
Emerging trends and technologies in AI QA
Opportunities for continued learning and development
• Project and Evaluation
Capstone project: deploying AI/ML in a QA scenario
Evaluation methods: quizzes, practical assessments, and peer reviews

Subjects

Programming