# An Exploratory Case Study of LLM-Assisted Refactoring and Gameplay Feature Generation in an Endless Runner Game

arXiv: https://arxiv.org/abs/2606.21171 · HTML: https://mlsanity.com/paper/2606.21171/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 19 Jun 2026.

## Builds on (most cited first, 15 of 15)

- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024)
- [The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development](https://mlsanity.com/paper/2302.07080/) (2023)
- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues](https://mlsanity.com/paper/2307.12596/) (2023)
- [No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPT](https://mlsanity.com/paper/2308.04838/) (2023)
- [On the Effectiveness of Large Language Models in Domain-Specific Code Generation](https://mlsanity.com/paper/2312.01639/) (2023)
- [LLMs for Science: Usage for Code Generation and Data Analysis](https://mlsanity.com/paper/2311.16733/) (2023)
- [Test-Driven Development for Code Generation](https://mlsanity.com/paper/2402.13521/) (2024)
- [Refactoring Programs Using Large Language Models with Few-Shot Examples](https://mlsanity.com/paper/2311.11690/) (2023)
- [An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Project](https://mlsanity.com/paper/2401.16186/) (2024)
- [Large Language Models and Video Games: A Preliminary Scoping Review](https://mlsanity.com/paper/2403.02613/) (2024)
- [An Empirical Study on the Code Refactoring Capability of Large Language Models](https://mlsanity.com/paper/2411.02320/) (2024)
- [Can ChatGPT Support Developers? An Empirical Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2402.11702/) (2024)
- [GPT for Games: An Updated Scoping Review (2020-2024)](https://mlsanity.com/paper/2411.00308/) (2024)
- [Leveraging Large Language Models for Efficient Failure Analysis in Game Development](https://mlsanity.com/paper/2406.07084/) (2024)

## Abstract (arXiv)

Large language models (LLMs) are increasingly used to support software 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 localized refactoring tasks and three tasks involving gameplay feature generation. The resulting implementations were evaluated using software metrics, unit tests, and manual gameplay assessments. In this case study, all three selected refactoring tasks were completed successfully in functional terms, whereas only one of the three selected gameplay feature generation tasks 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-assisted refactoring and gameplay feature generation in an existing game software system.