Usando LLMs para Programar Jogos de Tabuleiro e Variações
This addresses the time-consuming task of programming board games for developers, but appears incremental as it applies existing LLMs to a new domain without novel methodological contributions.
The authors tested the ability of three large language models (Claude, DeepSeek, ChatGPT) to generate code for board games and their variants, aiming to expedite the programming process, but the abstract does not provide concrete results or numbers.
Creating programs to represent board games can be a time-consuming task. Large Language Models (LLMs) arise as appealing tools to expedite this process, given their capacity to efficiently generate code from simple contextual information. In this work, we propose a method to test how capable three LLMs (Claude, DeepSeek and ChatGPT) are at creating code for board games, as well as new variants of existing games.