RESEARCH ON THE IMPACT OF LARGE LANGUAGE MODELS ON WEBSITE DEVELOPMENT USING THE VUE FRAMEWORK

Abstract

This paper investigates the transformative impact of large language models (LLMs) on modern component-based web development, using the Vue.js framework as a representative case study. By synthesizing the results of a broad empirical study on software development with the assistance of LLMs, we analyze a paradigmatic shift from native framework-driven workflows to workflows augmented by LLMs. The analysis spans the entire project lifecycle, revealing significant productivity gains during the implementation and deployment phases, particularly in component creation, test automation, and infrastructure configuration. However, these advantages are counterbalanced by critical challenges, including concerns about code reliability, the perpetuation of version conflicts due to outdated training data, and the risk of cognitive offloading among developers. We argue that the integration of LLMs redefines the role of the senior developer, transforming it from a primary code generator into an expert validator and architectural overseer. The paper concludes by outlining key risks and proposing directions for future research, emphasizing the need to develop framework-specific benchmarks for evaluating the quality of AI-generated code and to conduct longitudinal studies on the maintainability of Vue.js applications developed with the assistance of LLMs.