An Exploratory Case Study of LLM-Assisted Refactoring and Gameplay Feature Generation in an Endless Runner Game
Summary
This paper presents an exploratory case study evaluating GPT-4o's ability to perform refactoring and generate gameplay features in an endless runner game, finding that refactoring tasks succeeded while feature generation tasks mostly failed.
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Paper page - An Exploratory Case Study of LLM-Assisted Refactoring and Gameplay Feature Generation in an Endless Runner Game
Source: https://huggingface.co/papers/2606.21171
Abstract
Large language models demonstrate varying effectiveness in software development tasks, successfully completing localized refactoring but showing limitations in integrating new gameplay features within existing game systems.
Large language models(LLMs) are increasingly used to supportsoftware development, but their practical usefulness in applied game-development settings remains underexplored, especially when generated code must be integrated into an existing game software system. This paper presents an exploratory empirical case study of GPT-4o in a custom Python/Pygame endless runner. The study examines six selected development tasks: three localizedrefactoringtasks and three tasks involvinggameplay feature generation. The resulting implementations were evaluated usingsoftware metrics,unit tests, and manual gameplay assessments. In this case study, all three selectedrefactoringtasks were completed successfully in functional terms, whereas only one of the three selectedgameplay feature generationtasks resulted in a correctly integrated feature. The findings suggest that, in this setting, GPT-4o handled localized transformations more reliably than tasks requiring new gameplay interactions across multiple existing systems. Given the exploratory single-case design, these results are best interpreted as indicative observations rather than as generalizable evidence of category-level model performance. Overall, the paper contributes a transparent case-based account of the opportunities and limitations of LLM-assistedrefactoringandgameplay feature generationin an existing game software system.
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