Advancing software engineering through AI, federated learning, and large language models:
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. It equips readers with the knowledge and practical insights needed to harn...
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| Other Authors: | , , , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA)
IGI Global
2024.
|
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | 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. It equips readers with the knowledge and practical insights needed to harness these technologies effectively, enhancing software development, testing, maintenance, and deployment processes. 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. |
| Physical Description: | 28 PDFs (354 Seiten) Also available in print. |
| Format: | Mode of access: World Wide Web. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9798369335031 |
| Access: | Restricted to subscribers or individual electronic text purchasers. |
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| 246 | 3 | |a Advancing software engineering through artificial intelligence, federated learning, and large language models | |
| 264 | 1 | |a Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) |b IGI Global |c 2024. | |
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| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Chapter 1. Introduction to AI, ML, federated learning, and LLM in software engineering -- Chapter 2. A comprehensive review on large language models: exploring applications, challenges, limitations, and future prospects -- 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: blockchain integration for modern business -- Chapter 7. Machine learning for software engineering: models, methods, and applications -- Chapter 8. Industry-specific applications of AI and ML -- Chapter 9. Efficient software cost estimation using artificial intelligence: incorporating hybrid fuzzy modelling -- Chapter 10. Mobile app testing and the AI advantage in mobile app fine-tuning: elevate your app with AI testing -- Chapter 11. Reinforcement learning in bug triaging: addressing the cold start problem and beyond -- Chapter 12. Enhancing software testing through artificial intelligence: a comprehensive review -- 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: dimensions and challenges of a new populist business model -- 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: an approach to a sustainable renewable energy future -- Chapter 19. Artificial intelligence-internet of things integration for smart marketing: challenges and opportunities -- Chapter 20. Machine learning-based sentiment analysis of twitter using logistic regression. | |
| 506 | |a Restricted to subscribers or individual electronic text purchasers. | ||
| 520 | 3 | |a 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. It equips readers with the knowledge and practical insights needed to harness these technologies effectively, enhancing software development, testing, maintenance, and deployment processes. 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. | |
| 530 | |a Also available in print. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 588 | |a Description based on title screen (IGI Global, viewed 05/17/2024). | ||
| 650 | 0 | |a Software engineering. | |
| 653 | |a AI for bug detection and resolution in software engineering. | ||
| 653 | |a AI-enhanced software development. | ||
| 653 | |a Application of machine learning for software engineers. | ||
| 653 | |a Emerging trends in AI, ML, federated learning, and LLM. | ||
| 653 | |a Enhancing software reliability with ML. | ||
| 653 | |a Ethical implications of AI and ML in agile development. | ||
| 653 | |a Federated learning for collaborative open-source projects. | ||
| 653 | |a Federated learning use cases in software engineering. | ||
| 653 | |a Future trends in AI, ML, federated learning, and LLM. | ||
| 653 | |a Industry-specific applications of AI and ML. | ||
| 653 | |a Introduction to AI, ML, federated learning, and LLM in software engineering. | ||
| 653 | |a Introduction to lare language models (LLM) in software engineering. | ||
| 653 | |a REgression testing with AI and ML. | ||
| 653 | |a Security measures in AI and ML software development. | ||
| 653 | |a Software testing and quality assurance with ML. | ||
| 655 | 4 | |a Electronic books. | |
| 700 | 1 | |a Chanderwal, Nitin |d 1978- |e editor. | |
| 700 | 1 | |a Kansal, Mrignainy |e editor. | |
| 700 | 1 | |a Prajapati, Amarjeet |e editor. | |
| 700 | 1 | |a Sharma, Avinash Kumar |d 1982- |e editor. | |
| 700 | 1 | |a Singh, Pancham |e editor. | |
| 710 | 2 | |a IGI Global, |e publisher. | |
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| contents | Chapter 1. Introduction to AI, ML, federated learning, and LLM in software engineering -- Chapter 2. A comprehensive review on large language models: exploring applications, challenges, limitations, and future prospects -- 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: blockchain integration for modern business -- Chapter 7. Machine learning for software engineering: models, methods, and applications -- Chapter 8. Industry-specific applications of AI and ML -- Chapter 9. Efficient software cost estimation using artificial intelligence: incorporating hybrid fuzzy modelling -- Chapter 10. Mobile app testing and the AI advantage in mobile app fine-tuning: elevate your app with AI testing -- Chapter 11. Reinforcement learning in bug triaging: addressing the cold start problem and beyond -- Chapter 12. Enhancing software testing through artificial intelligence: a comprehensive review -- 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: dimensions and challenges of a new populist business model -- 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: an approach to a sustainable renewable energy future -- Chapter 19. Artificial intelligence-internet of things integration for smart marketing: challenges and opportunities -- Chapter 20. Machine learning-based sentiment analysis of twitter using logistic regression. |
| ctrlnum | (CaBNVSL)slc00005945 (OCoLC)1432782327 |
| dewey-full | 005.1028 |
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| discipline | Informatik |
| format | Electronic eBook |
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| spelling | Advancing software engineering through AI, federated learning, and large language models Avinash Kumar Sharma, Nitin Chanderwal, Amarjeet Prajapati, Pancham Singh, Mrignainy Kansal. Advancing software engineering through artificial intelligence, federated learning, and large language models Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) IGI Global 2024. 28 PDFs (354 Seiten) text rdacontent electronic isbdmedia online resource rdacarrier Includes bibliographical references and index. Chapter 1. Introduction to AI, ML, federated learning, and LLM in software engineering -- Chapter 2. A comprehensive review on large language models: exploring applications, challenges, limitations, and future prospects -- 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: blockchain integration for modern business -- Chapter 7. Machine learning for software engineering: models, methods, and applications -- Chapter 8. Industry-specific applications of AI and ML -- Chapter 9. Efficient software cost estimation using artificial intelligence: incorporating hybrid fuzzy modelling -- Chapter 10. Mobile app testing and the AI advantage in mobile app fine-tuning: elevate your app with AI testing -- Chapter 11. Reinforcement learning in bug triaging: addressing the cold start problem and beyond -- Chapter 12. Enhancing software testing through artificial intelligence: a comprehensive review -- 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: dimensions and challenges of a new populist business model -- 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: an approach to a sustainable renewable energy future -- Chapter 19. Artificial intelligence-internet of things integration for smart marketing: challenges and opportunities -- Chapter 20. Machine learning-based sentiment analysis of twitter using logistic regression. Restricted to subscribers or individual electronic text purchasers. 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. It equips readers with the knowledge and practical insights needed to harness these technologies effectively, enhancing software development, testing, maintenance, and deployment processes. 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. Also available in print. Mode of access: World Wide Web. Description based on title screen (IGI Global, viewed 05/17/2024). Software engineering. AI for bug detection and resolution in software engineering. AI-enhanced software development. Application of machine learning for software engineers. Emerging trends in AI, ML, federated learning, and LLM. Enhancing software reliability with ML. Ethical implications of AI and ML in agile development. Federated learning for collaborative open-source projects. Federated learning use cases in software engineering. Future trends in AI, ML, federated learning, and LLM. Industry-specific applications of AI and ML. Introduction to AI, ML, federated learning, and LLM in software engineering. Introduction to lare language models (LLM) in software engineering. REgression testing with AI and ML. Security measures in AI and ML software development. Software testing and quality assurance with ML. Electronic books. Chanderwal, Nitin 1978- editor. Kansal, Mrignainy editor. Prajapati, Amarjeet editor. Sharma, Avinash Kumar 1982- editor. Singh, Pancham editor. IGI Global, publisher. Print version: 9798369335024 |
| spellingShingle | Advancing software engineering through AI, federated learning, and large language models Chapter 1. Introduction to AI, ML, federated learning, and LLM in software engineering -- Chapter 2. A comprehensive review on large language models: exploring applications, challenges, limitations, and future prospects -- 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: blockchain integration for modern business -- Chapter 7. Machine learning for software engineering: models, methods, and applications -- Chapter 8. Industry-specific applications of AI and ML -- Chapter 9. Efficient software cost estimation using artificial intelligence: incorporating hybrid fuzzy modelling -- Chapter 10. Mobile app testing and the AI advantage in mobile app fine-tuning: elevate your app with AI testing -- Chapter 11. Reinforcement learning in bug triaging: addressing the cold start problem and beyond -- Chapter 12. Enhancing software testing through artificial intelligence: a comprehensive review -- 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: dimensions and challenges of a new populist business model -- 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: an approach to a sustainable renewable energy future -- Chapter 19. Artificial intelligence-internet of things integration for smart marketing: challenges and opportunities -- Chapter 20. Machine learning-based sentiment analysis of twitter using logistic regression. Software engineering. |
| title | Advancing software engineering through AI, federated learning, and large language models |
| title_alt | Advancing software engineering through artificial intelligence, federated learning, and large language models |
| title_auth | Advancing software engineering through AI, federated learning, and large language models |
| title_exact_search | Advancing software engineering through AI, federated learning, and large language models |
| title_full | Advancing software engineering through AI, federated learning, and large language models Avinash Kumar Sharma, Nitin Chanderwal, Amarjeet Prajapati, Pancham Singh, Mrignainy Kansal. |
| title_fullStr | Advancing software engineering through AI, federated learning, and large language models Avinash Kumar Sharma, Nitin Chanderwal, Amarjeet Prajapati, Pancham Singh, Mrignainy Kansal. |
| title_full_unstemmed | Advancing software engineering through AI, federated learning, and large language models Avinash Kumar Sharma, Nitin Chanderwal, Amarjeet Prajapati, Pancham Singh, Mrignainy Kansal. |
| title_short | Advancing software engineering through AI, federated learning, and large language models |
| title_sort | advancing software engineering through ai federated learning and large language models |
| topic | Software engineering. |
| topic_facet | Software engineering. Electronic books. |
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