Large Language Model-Based Intelligent Code Generation and Automated Software Engineering Framework

Abstract

Large Language Models (LLMs) have emerged as transformative technologies in modern artificial intelligence and software engineering by enabling intelligent code generation, automated debugging, software documentation, program synthesis, test case generation, and adaptive software optimization across large-scale development ecosystems. The rapid growth of cloud-native systems, distributed software infrastructures, DevOps pipelines, cybersecurity applications, autonomous systems, and enterprise-scale digital platforms has significantly increased the demand for scalable and intelligent software engineering frameworks capable of improving software productivity, code quality, development efficiency, and adaptive system maintenance. Traditional software engineering approaches frequently rely on manual coding, static rule-based automation, and developer-intensive debugging procedures, which often introduce scalability limitations, development delays, inconsistent software quality, and high maintenance overhead in complex computing environments. Recent advancements in transformer-based large language models have demonstrated remarkable capability in understanding programming semantics, generating executable code, performing automated reasoning, and supporting intelligent software development workflows through contextual natural language understanding and adaptive sequence modeling. This research proposes a Large Language Model-Based Intelligent Code Generation and Automated Software Engineering Framework designed to support scalable, adaptive, and explainable software development across heterogeneous computing ecosystems.

Large Language Models, Intelligent Code Generation, Automated Software Engineering, Transformer Architectures, Program Synthesis.