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<p>Large Language Models have received a lot of attention in recent years due to their outstanding performance on various Natural Language Processing tasks. They can be used for lots of applications, including assistance in code generation tasks. Actual literature lacks an exhaustive analysis of the benefits and drawbacks of using a Large Language Model for the generation of simple and complex code. This paper aims to overcome the issue: we perform a Literature Review to explore the state-of-the-art of the proposed topic, answering 4 Research Questions. Using the PRISMA methodology, we reviewed 66 papers published between 2021 and 2023. Our analysis reveals Python’s dominance as the preferred language and identifies a significant research gap in addressing ethical constraints. Additionally, we provide insights into the performance of models such as GPT-4 and CodeLlama, and their comparative utility in tasks ranging from debugging to multi-turn program synthesis. The findings offer a foundation for future research aimed at optimizing LLMs for code generation.</p>

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Usage of Large Language Model for Code Generation Tasks: A Review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月23日

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  • Stefano Bistarelli,
  • Marco Fiore,
  • Ivan Mercanti,
  • Marina Mongiello

摘要

Large Language Models have received a lot of attention in recent years due to their outstanding performance on various Natural Language Processing tasks. They can be used for lots of applications, including assistance in code generation tasks. Actual literature lacks an exhaustive analysis of the benefits and drawbacks of using a Large Language Model for the generation of simple and complex code. This paper aims to overcome the issue: we perform a Literature Review to explore the state-of-the-art of the proposed topic, answering 4 Research Questions. Using the PRISMA methodology, we reviewed 66 papers published between 2021 and 2023. Our analysis reveals Python’s dominance as the preferred language and identifies a significant research gap in addressing ethical constraints. Additionally, we provide insights into the performance of models such as GPT-4 and CodeLlama, and their comparative utility in tasks ranging from debugging to multi-turn program synthesis. The findings offer a foundation for future research aimed at optimizing LLMs for code generation.