SMART SOFTWARE ENGINEERING WITH AI-BASED DATA INTEGRATION APPROACHES

The rapid convergence of software engineering and artificial intelligence has fundamentally altered how modern digital systems are designed, built, and maintained. Traditional software development paradigms, while historically effective, increasingly struggle to handle the sheer volume, heterogeneity, and real-time demands of enterprise data. Smart Software Engineering with AI-Based Data Integration Approaches bridges this critical divide, presenting a unified framework for architecting adaptive, context-aware, and self-learning systems through intelligent data management. This book guides readers through the evolution of development practices—tracing the path from classical Waterfall and Agile methodologies to AI-augmented engineering lifecycles. Beginning with core foundations, the text delves deeply into specialized AI techniques for data discovery, schema matching, semantic entity resolution, knowledge graphs, and automated data quality enhancement. To ensure these techniques scale effectively in production environments, subsequent chapters detail modern architectural paradigms, including microservices, event-driven systems, edge computing, lakehouses, and MLOps pipelines. Theory is reinforced with real-world execution across diverse domains. Dedicated sections analyze how AI-driven data integration transforms clinical decision support in healthcare, risk analytics in finance, predictive operations in smart manufacturing, and urban data platforms in smart cities. Comparative global and regional case studies illustrate practical implementations, trade-offs, and operational lessons from the field. Looking toward the horizon, the final section examines the emerging frontiers re-defining software engineering, including Generative AI, Large Language Models, autonomous Agentic workflows, explainable AI, privacy-preserving federated integration, and sustainable, green computing practices. Designed for software architects, data engineers, researchers, and advanced students, this volume serves as both a rigorous reference and a strategic roadmap for designing the next generation of intelligent, data-centric software ecosystems.

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