Generative AI-Era Software Paradigm Reconstruction: A GAISF (Generative AI-Based Intelligent Software Fusion Framework) Theory and Model Study

Against the backdrop of the rapid development of generative artificial intelligence, traditional software engineering is undergoing a profound transformation from "function-driven" to "generative-driven." Software is no longer statically written by developers, but dynamically generated and continuously evolved by generative AI under the combined influence of user needs, contextual environment, and data resources. To address this trend, this paper proposes a new theoretical model—GAISF (Generative AI-based Intelligent Software Fusion Framework). This framework constructs a closed-loop system of "requirements understanding—code generation—runtime feedback—adaptive evolution," and introduces a software generation intensity function and an adaptive evolution mechanism to describe the dynamic behavior of software systems in the generative AI era. Through a reconstruction analysis of the traditional software engineering paradigm, this paper further explores the application value of GAISF in intelligent application development, enterprise software systems, and adaptive service systems. Research shows that this framework can significantly improve software development efficiency, system adaptability, and continuous evolution capabilities, providing a new theoretical foundation for future software engineering.