Cloud-native Architecture (CNA) and Artificial Intelligence (AI) for the Future of Software Engineering: The Principles, Patterns, Platforms and Practices
Elsevier (Publisher)
Published on 2. March 2026
310 pages
978-0-443-22402-7 (ISBN)
Description
Cloud-native Architecture (CNA) and Artificial Intelligence (AI) for the Future of Software Engineering: The Principles, Patterns, Platforms and Practices, Volume 141 in the Advances in Computers series, explores the convergence of artificial intelligence, machine learning, and modern software engineering practices. Chapters in this new release include Demystifying the Cloud-native Artificial Intelligence (CNAI) Paradigm, Articulating Machine and Deep Learning Models for Next-Generation Software Development, Delineating Artificial Intelligence (AI) and Its Potentials for Automated Software Engineering, Leveraging Machine and Deep Learning (ML/DL) Algorithms towards AI Models for Automating Software Development, and more.Other sections cover Artificial Intelligence (AI) Technologies and Tools for Accelerated Software Development, Demystifying the Agentic AI Paradigm for Accelerated Software Engineering, Detailing AI Techniques and Tools for Software Engineering Acceleration and Automation, Generative AI Tools for Accelerated Software Engineering, Empowering Software Engineering Automation through Explainable AI, and much more.
- Contains novel subject matter that is relevant to computer science
- Includes the expertise of contributing authors
- Presents an easy to comprehend writing style
More details
Other editions
Additional editions
Mats Agerstam | Pushan Kumar Dutta | Balasubramanian Sundaravadivazhagan
Cloud-native Architecture (CNA) and Artificial Intelligence (AI) for the Future of Software Engineering: The Principles, Patterns, Platforms and Practices: Volume 141
Book
approx. 03/2026
Academic Press
€155.99
Not yet published
Persons
Content
1. Demystifying the Cloud-native Artificial Intelligence (CNAI) Paradigm2. Articulating Machine and Deep Learning Models for Next-Generation Software Development3. Delineating Artificial Intelligence (AI) and Its Potentials for Automated Software Engineering4. Leveraging Machine and Deep Learning (ML/DL) Algorithms towards AI Models for Automating Software Development5. Artificial Intelligence (AI) Technologies and Tools for Accelerated Software Development6. Demystifying the Agentic AI Paradigm for Accelerated Software Engineering7. Detailing AI Techniques and Tools for Software Engineering Acceleration and Automation8. Generative AI Tools for Accelerated Software Engineering9. Empowering Software Engineering Automation through Explainable AI10. Deepseek and ChatGPT for Software Engineering: An Analysis and Insight11. Agile Software Development through DevSecOps, Site Reliability Engineering, and Value Stream Management (VSM) Products and Practices12. Evolution of Software Engineering: From Traditional to Modern Approaches13. Cloud Native Engineering: A Comprehensive Review of Principles, Practices, and Challenges14. AI-Driven Testing Practices for Cloud-Native Systems15. Kubernetes for Highly Scalable and Self-Healing Software Systems16. Pioneering Agile Innovation: A Cloud-Native MLOps Odyssey for Data-Driven Applications17. Cloud Native Engineering for AI-Driven ERP Systems: Enhancing Security, Compliance, and Data Privacy18. Cloud-Native Secrets Management: Eliminating Hardcoded Secrets in CI/CD and Cloud Workloads with HashiCorp Vault19. Ethical AI and Responsible Automation in Software Engineering
