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

Researchers conducted a case study evaluating GPT-4o's effectiveness in game development tasks within an existing Python/Pygame endless runner project. The study found that while the model successfully completed all three refactoring tasks, only one of three gameplay feature generation tasks integrated correctly, suggesting LLMs perform better with localized code transformations than complex cross-system integrations.

This empirical case study addresses a critical gap in understanding how large language models perform in real-world game development environments. Rather than testing LLMs in isolation, the researchers evaluated GPT-4o's ability to work within an existing software system—a far more practical scenario than generating standalone code snippets. The distinction between refactoring and feature generation proved decisive: localized code improvements, where context is contained and dependencies are minimal, aligned with the model's strengths, while feature generation requiring understanding of multiple interconnected game systems exposed its limitations.

The broader context reflects growing adoption of LLMs in software development workflows, yet most evaluation focuses on simplistic benchmarks rather than integrated systems. Game development presents a particularly demanding use case because code must interact seamlessly with physics engines, asset management, event systems, and gameplay logic. This study's findings—100% success on refactoring versus 33% on feature generation—suggest that LLM usefulness varies dramatically depending on task scope and architectural complexity.

For the game development industry, these results indicate that LLMs function best as assistants for code cleanup and optimization rather than autonomous feature architects. Developers should view GPT-4o as a productivity tool for technical debt reduction while maintaining skepticism about its ability to generate novel gameplay systems. The single-case design limits generalizability, but the transparent methodology provides a replicable framework for future research across different game engines and development contexts.

• →GPT-4o achieved 100% success on isolated refactoring tasks but only 33% on gameplay feature generation requiring multi-system integration.
• →LLMs demonstrate stronger performance in localized code transformations than in tasks requiring understanding of complex architectural dependencies.
• →Game development represents a domain where LLM limitations become apparent when integration with existing systems is required.
• →The case study methodology provides a reproducible framework for evaluating LLMs in real-world software development contexts rather than isolated benchmarks.
• →Developers should treat LLMs as code optimization assistants rather than autonomous feature architects for complex interactive systems.