Large Language Models for Game Development: A Survey on Automated Code Generation

TY - GEN

T1 - Large Language Models for Game Development: A Survey on Automated Code Generation

AU - Dastmalchi Saei, Alireza

AU - Sharbaf, Mohammadreza

AU - Kolahdouz Rahimi, Shekoufeh

PY - 2025

Y1 - 2025

N2 - Large Language Models (LLMs) are increasingly utilised in software development, particularly for automatic code generation. Game development, a complex and multidisciplinary field, has begun leveraging LLMs to streamline various stages of the development process. This paper focuses on the application of LLMs in automating code generation for game development, providing a comprehensive survey of existing research from multiple perspectives. We analyse the benefits and limitations of LLM-based approaches in this domain and identify key challenges. Our findings highlight the potential of LLMs to enhance game development workflows, while outlining future research directions to address existing limitations.

AB - Large Language Models (LLMs) are increasingly utilised in software development, particularly for automatic code generation. Game development, a complex and multidisciplinary field, has begun leveraging LLMs to streamline various stages of the development process. This paper focuses on the application of LLMs in automating code generation for game development, providing a comprehensive survey of existing research from multiple perspectives. We analyse the benefits and limitations of LLM-based approaches in this domain and identify key challenges. Our findings highlight the potential of LLMs to enhance game development workflows, while outlining future research directions to address existing limitations.

UR - https://conf.researchr.org/home/staf-2025/llm4se-2025#event-overview

M3 - Conference contribution

BT - First Large Language Models for Software Engineering Workshop (LLM4SE 2025)

PB - CEUR Workshop Proceedings

ER -