Large-Scale Integration of Large Language Models into Software Engineering: Toward a Comprehensive Framework for Testing, Evaluation, and Deployment - Dr. Arjun Mehta (Emerging Frontiers Library for The American Journal of Interdisciplinary Innovations and Research, 2025)
With the rapid evolution and proliferation of Large Language Models (LLMs) in natural language processing, researchers and practitioners increasingly explore their potential in software engineering domains such as code generation, automated testing, and deployment workflows. This article presents a comprehensive conceptual analysis integrating insights from recent surveys and empirical studies to propose a unified framework for effectively leveraging LLMs across the software development lifecycle. Drawing on major works, including the broad survey of LLM architectures and capabilities (Zhao et al., 2024), the domain‐specific evaluation of code generation tasks (Chen et al., 2024), and in‐depth analyses of software testing with LLMs (Wang et al., 2024; Fan et al., 2023; Hou et al., 2024), this research systematically synthesizes existing findings, identifies critical gaps, and outlines a structured methodology to address key challenges. The findings highlight substantial variability in evaluation standards, a lack of robust testing pipelines tailored to LLM-generated code, deployment scalability constraints, and limited consensus on best practices. The proposed framework encompasses taxonomy, evaluation guidelines, testing strategies, and deployment infrastructure recommendations. This framework aims to guide future empirical research, industrial adoption, and standardization efforts in integrating LLM-powered tools into software engineering. The article concludes by discussing limitations and suggesting directions for future work, including empirical validation, benchmarking protocols, and governance considerations.