Название: Boosting Software Development Using Machine Learning
Автор: Tirimula Rao Benala, Satchidananda Dehuri, Rajib Mall, Margarita N. Favorskaya
Издательство: Springer
Серия: Artificial Intelligence-Enhanced Software and Systems Engineering
Год: 2025
Страниц: 333
Язык: английский
Формат: pdf (true), epub
Размер: 25.2 MB

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.

The rapid evolution of software engineering has been profoundly influenced by the transformative role of Machine Learning (ML) and Artificial Intelligence (AI). Indeed, practitioners and researchers increasingly leverage data-driven methods to streamline every phase of the software development life cycle (SDLC)—from requirements elicitation and architectural design to coding, testing, and deployment. This volume, Boosting Software Development Using Machine Learning, brings together a collection of original research studies and insightful case analyses that underscore the profound impact of AI-ML in contemporary software engineering. This volume aims to serve as a valuable resource for academics, industry professionals, and students eager to deepen their understanding of AI-ML-enhanced software development practices by showcasing a broad range of theoretical foundations, practical applications, and empirical findings.