Generative AI for Code Synthesis: A Comparative Study of Large Language Models in Software Engineering

Generative AI for Code Synthesis: A Comparative Study of Large Language Models in Software Engineering

 Author: Parth Bhatt, Co – Author: Piyush Kashiyani.

Department of Information Technology, Atmiya University, Rajkot, Gujarat, India

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Abstract - This paper presents a comprehensive analysis of large language models (LLMs) in automated code generation, comparing architectural approaches, security implications, and practical efficacy. Through systematic evaluation of GPT-4, GLM-4, CodeGen, and Polycoder models, we identify key performance metrics in code quality, vulnerability rates, and development efficiency. Our findings reveal a 30-100x efficiency gain in AI-assisted programming compared to traditional methods, with GPT-4 achieving 42% vulnerability-free code versus CodeGen's 39% in Python implementations. The study highlights the critical need for robust validation frameworks when deploying LLMs in software engineering workflows.

Keywords:  Analysis, Investigation, Research, Code Generation, Vulnerability Detection, Development Efficiency