國立虎尾科技大學 |

Boosting software development using machine learning

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Boosting software development using machine learning/ edited by Tirimula Rao Benala ... [et al.].
其他作者:	Benala, Tirimula Rao.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xxii, 320 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Machine Learning. -
電子資源:	https://doi.org/10.1007/978-3-031-88188-6
ISBN:	9783031881886

Boosting software development using machine learning
Boosting software development using machine learning[electronic resource] /edited by Tirimula Rao Benala ... [et al.]. - Cham :Springer Nature Switzerland :2025. - xxii, 320 p. :ill. (some col.), digital ;24 cm. - Artificial intelligence-enhanced software and systems engineering,v. 72731-6033 ;. - Artificial intelligence-enhanced software and systems engineering ;v. 6..

1.Transforming Software Development: From Traditional Methods to Generative Artificial Intelligence -- 2.Case Study: Transforming Operational and Organizational Efficiency Using Artificial Intelligence and Machine Learning -- 3.Revolutionizing Software Development: The Transformative Influence of Machine Learning Integrated SDLC Model -- 4.Generative Coding: Unlocking Ontological AI -- 5.Case Studies: Machine Learning Approaches for Software Development Effort Estimation -- 6.Hybridizing Metaheuristics and Analogy-based Methods with Ensemble Learning for Improved Software Cost Estimation -- 7.A Review on Detection of Design Pattern in Source Code Using Machine Learning Techniques -- 8.Machine Learning Techniques for the Measurement of Software Attributes -- 9.An Effective Analysis of New Metaheuristic Algorithms and its Performance Comparison -- 10.Empowering Software Security: Leveraging Machine Learning for Anomaly Detection and Threat Prevention -- 11.Sentiment Analysis on Movie Reviews Using the Convolutional LSTM (Co-LSTM) Model -- 12.An Overview of AI Workload Optimization Techniques -- 13.Opportunity Discovery for Effective Innovation Using Artificial Intelligence -- 14.Applications of Machine Learning Algorithms in Open Innovation.

This book explores the transformative effects of AI and ML on software engineering. It emphasizes the potential of cutting-edge software development technologies such as Generative AI and ML applications. This book incorporates data-driven strategies across the entire software development life cycle, from requirements elicitation and design to coding, testing, and deployment. It illustrates the evolution from traditional frameworks to agile and DevOps methodologies. The potential of Generative AI for automating repetitive tasks and enhancing code quality is highlighted, along with ML applications in optimizing testing, effort estimation, design pattern recognition, fault prediction, debugging, and security through anomaly detection. These techniques have significantly improved software development efficiency, predictability, and project management effectiveness. While remarkable progress has been made, much remains to be done in this evolving area. This edited book is a timely effort toward advancing the field and promoting interdisciplinary collaboration in addressing ethical, security, and technical challenges.

ISBN: 9783031881886

Standard No.: 10.1007/978-3-031-88188-6doiSubjects--Topical Terms:

1137723
Machine Learning.

LC Class. No.: QA76.76.D47

Dewey Class. No.: 005.1

Boosting software development using machine learning
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520 $a This book explores the transformative effects of AI and ML on software engineering. It emphasizes the potential of cutting-edge software development technologies such as Generative AI and ML applications. This book incorporates data-driven strategies across the entire software development life cycle, from requirements elicitation and design to coding, testing, and deployment. It illustrates the evolution from traditional frameworks to agile and DevOps methodologies. The potential of Generative AI for automating repetitive tasks and enhancing code quality is highlighted, along with ML applications in optimizing testing, effort estimation, design pattern recognition, fault prediction, debugging, and security through anomaly detection. These techniques have significantly improved software development efficiency, predictability, and project management effectiveness. While remarkable progress has been made, much remains to be done in this evolving area. This edited book is a timely effort toward advancing the field and promoting interdisciplinary collaboration in addressing ethical, security, and technical challenges.
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