Cloud-native Architecture (CNA) and Artificial Intelligence (AI) for the Future of Software Engineering: The Principles, Patterns, Platforms and Practices

1. Demystifying the Cloud-native Artificial Intelligence (CNAI) Paradigm<br>2. Articulating Machine and Deep Learning Models for Next-Generation Software Development<br>3. Delineating Artificial Intelligence (AI) and Its Potentials for Automated Software Engineering<br>4. Leveraging Machine and Deep Learning (ML/DL) Algorithms towards AI Models for Automating Software Development<br>5. Artificial Intelligence (AI) Technologies and Tools for Accelerated Software Development<br>6. Demystifying the Agentic AI Paradigm for Accelerated Software Engineering<br>7. Detailing AI Techniques and Tools for Software Engineering Acceleration and Automation<br>8. Generative AI Tools for Accelerated Software Engineering<br>9. Empowering Software Engineering Automation through Explainable AI<br>10. Deepseek and ChatGPT for Software Engineering: An Analysis and Insight<br>11. Agile Software Development through DevSecOps, Site Reliability Engineering, and Value Stream Management (VSM) Products and Practices<br>12. Evolution of Software Engineering: From Traditional to Modern Approaches<br>13. Cloud Native Engineering: A Comprehensive Review of Principles, Practices, and Challenges<br>14. AI-Driven Testing Practices for Cloud-Native Systems<br>15. Kubernetes for Highly Scalable and Self-Healing Software Systems<br>16. Pioneering Agile Innovation: A Cloud-Native MLOps Odyssey for Data-Driven Applications<br>17. Cloud Native Engineering for AI-Driven ERP Systems: Enhancing Security, Compliance, and Data Privacy<br>18. Cloud-Native Secrets Management: Eliminating Hardcoded Secrets in CI/CD and Cloud Workloads with HashiCorp Vault<br>19. Ethical AI and Responsible Automation in Software Engineering