Advancing Software Engineering Through AI, Federated Learning, and Large Language Models
1st ed.. - Hershey: IGI Global, 2024
Online
Monographie, Elektronische Ressource
- 1 online resource (375 pages)
Ermittle Ausleihstatus...
The rapid evolution of software engineering demands innovative approaches to meet the growing complexity and scale of modern software systems. Traditional methods often need help to keep pace with the demands for efficiency, reliability, and scalability. Manual development, testing, and maintenance processes are time-consuming and error-prone, leading to delays and increased costs. Additionally, integrating new technologies, such as AI, ML, Federated Learning, and Large Language Models (LLM), presents unique challenges in terms of implementation and ethical considerations. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models provides a compelling solution by comprehensively exploring how AI, ML, Federated Learning, and LLM intersect with software engineering. By presenting real-world case studies, practical examples, and implementation guidelines, the book ensures that readers can readily apply these concepts in their software engineering projects. Researchers, academicians, practitioners, industrialists, and students will benefit from the interdisciplinary insights provided by experts in AI, ML, software engineering, and ethics.
Title Page -- Copyright Page -- Book Series -- Editorial Advisory Board -- Table of Contents -- Detailed Table of Contents -- Preface -- Chapter 1: Introduction to AI, ML, Federated Learning, and LLM in Software Engineering -- Chapter 2: A Comprehensive Review on Large Language Models -- Chapter 3: Software Engineering Strategies for Real-Time Personalization in E-Commerce Recommendations -- Chapter 4: Application of Machine Learning for Software Engineers -- Chapter 5: AI-Driven Software Development Lifecycle Optimization -- Chapter 6: Artificial Intelligence -- Chapter 7: Machine Learning for Software Engineering -- Chapter 8: Industry-Specific Applications of AI and ML -- Chapter 9: Efficient Software Cost Estimation Using Artificial Intelligence -- Chapter 10: Mobile App Testing and the AI Advantage in Mobile App Fine-Tuning -- Chapter 11: Reinforcement Learning in Bug Triaging -- Chapter 12: Enhancing Software Testing Through Artificial Intelligence -- Chapter 13: Enhancing Spoken Text With Punctuation Prediction Using N-Gram Language Model in Intelligent Technical Text Processing Software -- Chapter 14: SecureStem Software for Optimized Stem Cell Banking Management -- Chapter 15: Technology-Based Scalable Business Models -- Chapter 16: Test Data Generation for Branch Coverage in Software Structural Testing Based on TLBO -- Chapter 17: The Position of Digital Society, Healthcare 5.0, and Consumer 5.0 in the Era of Industry 5.0 -- Chapter 18: Green Software Engineering Development Paradigm -- Chapter 19: Artificial Intelligence-Internet of Things Integration for Smart Marketing -- Chapter 20: Machine Learning-Based Sentiment Analysis of Twitter Using Logistic Regression -- Compilation of References -- About the Contributors -- Index.
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Advancing Software Engineering Through AI, Federated Learning, and Large Language Models
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Autor/in / Beteiligte Person: | Sharma, Avinash Kumar ; Chanderwal, Nitin ; Prajapati, Amarjeet ; Singh, Pancham ; Kansal, Mrignainy |
Lokaler Link: | |
Ausgabe: | 1st ed. |
Veröffentlichung: | Hershey: IGI Global, 2024 |
Medientyp: | Monographie |
Datenträgertyp: | Elektronische Ressource |
Umfang: | 1 online resource (375 pages) |
ISBN: | 979-83-69335-04-8; 979-83-69335-03-1 |
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