MULTI-LANGUAGE CODE GENERATION WITH CHATGPT: INSIGHTS INTO AI'S CROSS-LINGUSTIC PROGRAMMING ABILITIES

Abstract

The rapid advancement of Artificial Intelligence (AI) and Large Language Models (LLMs) has revolutionized software development by enabling intelligent code generation across multiple programming languages. Among these models, ChatGPT has emerged as a powerful AI-assisted programming tool capable of understanding natural language instructions and automatically generating syntactically correct, context-aware, and executable code in languages such as Python, Java, C++, JavaScript, C#, Go, PHP, and SQL. Traditional software development often requires developers to possess expertise in multiple programming languages, increasing development time and complexity. AI-driven code generation addresses these challenges by automating code synthesis, debugging, documentation, code translation, and optimization through natural language interaction. This paper proposes a comprehensive framework for evaluating ChatGPT's cross-linguistic programming capabilities across multiple programming languages. The proposed framework integrates natural language understanding, prompt engineering, semantic analysis, code generation, syntax validation, execution testing, and performance evaluation. Comparative analysis is performed between conventional rule-based code generation techniques and Large Language Model-based approaches. Experimental results demonstrate that ChatGPT significantly improves code generation accuracy, programming efficiency, syntax correctness, and developer productivity while reducing coding time and debugging effort. The proposed framework contributes to intelligent software engineering by enabling multilingual programming assistance, automated software development, educational support, and cross-platform application development. Furthermore, the research highlights the transformative role of generative AI in modern programming environments and intelligent code generation systems.