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. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xiii 7. Chapter One: Demystifying the cloud-native artificial intelligence (CNAI) paradigm - p. 1 8. Chapter Two: Articulating machine and deep learning models for next-generation software development - p. 31 9. Chapter Three: Delineating artificial intelligence and its engrossing potentials for automated software developments - p. 67 10. Chapter Four: Leveraging machine and deep learning (ML/DL) algorithms towards AI models for automating software development - p. 93 11. Chapter Five: Artificial intelligence (AI) technologies and tools for accelerated software development - p. 115 12. Chapter Six: Demystifying the agentic AI paradigm for accelerated software engineering - p. 161 13. Chapter Seven: Detailing AI techniques and tools for software engineering acceleration and automation - p. 183 14. Chapter Eight: Generative AI tools for accelerated software engineering - p. 211 15. Chapter Nine: Empowering software engineering automation through explainable Ai - p. 229 16. Chapter Ten: Deepseek and Chatgpt for software engineering: An analysis and insight - p. 257 17. Chapter Eleven: Agile Software Development: DevOps, SRE, and VSM Practices & Tools - p. 285 18. Chapter Twelve: Evolution of software engineering: From traditional to modern approaches - p. 309 19. Chapter Thirteen: Cloud native engineering: A comprehensive review of principles, practices, and challenges - p. 331 20. Chapter Fourteen: AI-driven testing for cloud-native architectures: From implementation challenges to integrated platforms - p. 355 21. Chapter Fifteen: Kubernetes for highly scalable and self-healing software systems - p. 383 22. Chapter Sixteen: Pioneering agile innovation: A cloud-native MLOps Odyssey for data-driven applications - p. 409 23. Chapter Seventeen: Cloud native engineering for AI-driven ERP systems: Enhancing security, compliance and data privacy - p. 439 24. Chapter Eighteen: Cloud-native secrets management: Eliminating hardcoded secrets with HashiCorp Vault - p. 465 25. Chapter Nineteen: Ethical AI and responsible automation in software engineering - p. 483
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. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xiii 7. Chapter One: Demystifying the cloud-native artificial intelligence (CNAI) paradigm - p. 1 8. Chapter Two: Articulating machine and deep learning models for next-generation software development - p. 31 9. Chapter Three: Delineating artificial intelligence and its engrossing potentials for automated software developments - p. 67 10. Chapter Four: Leveraging machine and deep learning (ML/DL) algorithms towards AI models for automating software development - p. 93 11. Chapter Five: Artificial intelligence (AI) technologies and tools for accelerated software development - p. 115 12. Chapter Six: Demystifying the agentic AI paradigm for accelerated software engineering - p. 161 13. Chapter Seven: Detailing AI techniques and tools for software engineering acceleration and automation - p. 183 14. Chapter Eight: Generative AI tools for accelerated software engineering - p. 211 15. Chapter Nine: Empowering software engineering automation through explainable Ai - p. 229 16. Chapter Ten: Deepseek and Chatgpt for software engineering: An analysis and insight - p. 257 17. Chapter Eleven: Agile Software Development: DevOps, SRE, and VSM Practices & Tools - p. 285 18. Chapter Twelve: Evolution of software engineering: From traditional to modern approaches - p. 309 19. Chapter Thirteen: Cloud native engineering: A comprehensive review of principles, practices, and challenges - p. 331 20. Chapter Fourteen: AI-driven testing for cloud-native architectures: From implementation challenges to integrated platforms - p. 355 21. Chapter Fifteen: Kubernetes for highly scalable and self-healing software systems - p. 383 22. Chapter Sixteen: Pioneering agile innovation: A cloud-native MLOps Odyssey for data-driven applications - p. 409 23. Chapter Seventeen: Cloud native engineering for AI-driven ERP systems: Enhancing security, compliance and data privacy - p. 439 24. Chapter Eighteen: Cloud-native secrets management: Eliminating hardcoded secrets with HashiCorp Vault - p. 465 25. Chapter Nineteen: Ethical AI and responsible automation in software engineering - p. 483
