AI-Driven Software Engineering: Harnessing Machine Learning for Smarter Development and Automation explores the transformative role of AI in modern software engineering. It begins by introducing AI's impact on software development, detailing its integration into the Software Development Lifecycle (SDLC) for enhancing requirements gathering, design, coding, and refactoring. Machine learning models and their applications in software engineering, including predictive analysis, testing automation, and bug detection, are covered extensively. The book delves into AI's contributions to project management, from resource allocation to risk mitigation, and its automation of DevOps workflows through intelligent CI/CD pipelines and self-healing infrastructure. It also highlights Natural Language Processing (NLP) applications, like automating code documentation and analyzing requirements. Ethical challenges, such as bias and privacy, are addressed alongside AI's role in software maintenance and future trends like quantum computing integration. With practical tools, case studies, and a forward-looking approach, this book is a comprehensive guide to AI-driven software engineering.
