Testing Smarter, Not Harder with AI - A Realistic Overview of What Is Possible Today

Overview

Explore AI's role in enhancing software testing efficiency, from test design to defect detection. Learn about current applications and future possibilities in quality assurance for IoT and robotics development.

Syllabus

• Introduction to AI in Software Testing
Overview of AI applications in quality assurance
Benefits of AI in testing processes
• AI-Enhanced Test Design
Automated test case generation
AI-driven test planning and prioritization
• AI in Test Execution
Intelligent test execution strategies
Dynamic testing with AI algorithms
• Defect Detection Using AI
Machine learning for anomaly detection
Predictive analytics in defect prediction
• AI for IoT Testing
Challenges of IoT testing
AI techniques specific to IoT environments
• AI in Robotics Testing
Role of AI in robotic systems validation
Case studies of AI in robotics quality assurance
• Tools and Technologies
Overview of AI tools for software testing
Hands-on demonstrations of popular AI-based testing tools
• Current Limitations and Future Possibilities
Challenges in AI-driven testing
Emerging trends and research directions
• Case Studies and Real-World Applications
Success stories of AI in software testing
Analysis of industry implementations
• Ethical and Practical Considerations
Ethical implications of AI in testing
Best practices for integrating AI responsibly
• Conclusion and Future Outlook
Recap of key takeaways
Insights into the future evolution of AI in software testing

Subjects

Conference Talks