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

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

Agerstam, Mats (EDT)/ Dutta, Pushan Kumar (EDT)/ Sundaravadivazhagan, Balasubramanian (EDT)

ウェブストア価格 ¥38,949（本体¥35,409）
Academic Press Inc（2026/03発売）
外貨定価 US$ 182.00
ポイント 354pt

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製本 Hardcover:ハードカバー版／ページ数 310 p.
言語 ENG
商品コード 9780443224010

Full 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.

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