Source-linked AI summary
An Exploratory Case Study of LLM-Assisted Refactoring and Gameplay Feature Generation in an Endless Runner Game
Jan Wunderlich, Markus Kleffmann, Sebastian Lempert
TL;DR
Evidence remains limited on how reliably LLMs perform game-development tasks integrated into existing game systems. This exploratory case study evaluates GPT-4o in a Python/Pygame endless runner and finds refactoring more reliable than gameplay feature generation.
Problem
Empirical evidence remains limited on how reliably LLMs perform game-development tasks requiring integration into existing game software systems.
Method
The study evaluates GPT-4o through an empirical case study of selected refactoring and gameplay feature-generation tasks in a Python/Pygame endless runner.
Results
All selected refactoring tasks succeeded functionally, whereas only one gameplay feature-generation task produced a correctly integrated feature.
Takeaways & Limitations
In this setting, GPT-4o was more reliable for localized code transformations than for integration-intensive gameplay extensions.
Takeaways & Limitations
The single-project, single-model design limits generalizability to other game contexts, models, and development settings.
Abstract
from arXiv · showhide
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.
1 Introduction
The paper addresses limited empirical evidence on LLM reliability in game-development tasks by studying GPT-4o in a custom Python/Pygame endless runner. It contrasts refactoring with gameplay feature generation and evaluates implementations through metrics, unit tests, and manual gameplay assessment.
- Motivation: Game development is a relevant testbed because generated code must interact with gameplay logic, visual systems, event-driven behavior, and interdependent runtime components.Games also combine structural complexity with recurring patterns and permit assessment beyond static inspection.
- Study setting: The study examines GPT-4o [14] in a custom endless runner implemented in Python and Pygame, using one existing game codebase.It compares modifying existing structures through refactoring with extending functionality through gameplay feature generation.
- Research questions: The two research questions ask whether GPT-4o can preserve functionality during refactoring and generate gameplay features within an existing game architecture.The task classes are treated as contrasting in scope, with refactoring framed as localized transformation and feature generation as integration-intensive extension.
- Method: The methodology comprises two structured experiments with three use cases per task class, evaluated using software metrics, unit tests, and manual gameplay assessments.This design supports evaluation of both code-oriented outcomes and gameplay behavior.
- Contribution: The paper contributes an exploratory empirical case study that transparently identifies where GPT-4o supported code-oriented game development and where integration challenges remained.The analysis focuses on selected refactoring and gameplay feature generation tasks within the same project context.
2 Related Work
Prior research has examined LLMs for software-engineering tasks and game-related applications, while showing that effectiveness depends on task and evaluation conditions. This paper addresses the limited understanding of how refactoring and gameplay feature generation behave within the same existing game system and validation setup.
- Software-engineering applications: Studies have examined LLMs for code generation, code transformation, and interactive development assistance, with effectiveness depending on task complexity, prompting strategy, and evaluation context [9] [10] [16].
- LLMs in digital games: Game-related research spans game development, game AI, narrative systems, and research applications, with empirical work also examining technical workflows such as failure analysis and debugging [3] [22].
- LLM-assisted refactoring: Prior refactoring studies found that LLMs can suggest simpler Python programs and identify refactoring opportunities, but unsafe or functionality-changing recommendations still occur [7] [19].
- Research gap: Existing research often studies refactoring, code generation, and game applications separately, leaving less known about localized transformations versus integration-intensive gameplay extensions in one existing system.This paper compares these contrasting task classes under a shared validation setup.
3 Methodology
The study evaluates GPT-4o through an exploratory empirical case study of refactoring and gameplay feature generation in an author-developed Python/Pygame endless runner. Six tasks were assessed using code metrics, tests, manual gameplay, and iterative validation under explicit functional-success criteria.
- Study design: GPT-4o was evaluated in an author-developed Python/Pygame endless runner using a structured empirical case-study design.The project provided a compact but sufficiently realistic setting for investigating LLM-assisted development tasks.
- Task design: The study selected three refactoring tasks and three gameplay feature-generation tasks spanning different technical scopes and integration depths.The tasks were not pre-calibrated for equivalent difficulty, so comparisons between task classes remain exploratory and qualitative rather than controlled benchmarks.
- Task design: Refactoring addressed central state-management optimization, redundant asset-loading elimination, and movement-control unification, while feature generation addressed pixel-perfect collision, a shrink power-up, and a car platform mechanic.These tasks varied in modification scope while remaining within the same game project.
- Experimental scope: GPT-4o was tested as a specific model snapshot, not as a proxy for newer models, specialized code models, or agentic coding tools.The model was selected for its availability and code-related capabilities at the time of experimentation.
- Implementation procedure: The complete 31,947-token codebase was supplied in one input, with each task implemented from the same main-branch state in a dedicated Git branch.An initial codebase-summary check supported interpretation of the project structure and dependencies, while separate branches enabled source-level traceability.
- Evaluation: Evaluation combined SonarQube and custom maintainability metrics with unit tests, feature-specific tests, manual gameplay assessments, and iterative refinement.Success required satisfying the functional requirement, passing relevant tests, and behaving correctly during gameplay; code-level tests alone were insufficient.
4 Results
In this case study, GPT-4o completed all three selected refactoring tasks functionally but correctly integrated only one of three gameplay feature-generation tasks. Refactoring was therefore more reliable than gameplay feature generation, whose failures involved deeper integration with existing game systems.
- Overall results: All three selected refactoring tasks succeeded functionally, whereas only one of three gameplay feature-generation tasks produced a correctly integrated feature.This comparison summarizes the overall outcomes reported for the evaluated tasks.
- Evaluation procedure: Iteration counts represented refinement cycles within single runs, with refactoring revisions driven mainly by missing functionality, compatibility issues, or test-detected regressions.They should not be interpreted as repeated independent model runs; gameplay failures involved deeper integration problems that persisted despite code revisions.
- Gameplay feature generation: Gameplay feature generation was less reliable: pixel-perfect collision detection succeeded after 4 iterations, while the shrink power-up and car platform mechanic remained unsuccessful.The shrink power-up failed after 8 refinement attempts; the car platform mechanic passed unit tests after 6 iterations but failed manual gameplay testing.
- Gameplay feature generation: The failed gameplay features exposed semantic integration problems across existing mechanics and architecture rather than merely superficial code-quality issues.Scaling changes interfered with jumping, sliding, and weapon behavior, while car landings caused death or incorrect movement during actual gameplay.
5 Discussion
In this case study, GPT-4o was more reliable for localized, well-bounded refactoring than for gameplay features requiring deep integration across interdependent systems. The findings also emphasize gameplay-level validation, human oversight, and cautious interpretation due to substantial study limitations.
- Interpretation: GPT-4o handled localized refactoring more reliably than new gameplay functionality, especially when changes were well-bounded and required limited integration across gameplay systems.Refactoring outcomes were functionally stable, with structural improvements in several cases, whereas complex gameplay features required greater coordination.
- Practical implications: LLMs may support localized code improvement, exploratory restructuring, and alternative implementations, but complex gameplay features are not immediately production-ready.Such features require careful integration, testing, developer supervision, and significant human oversight.
- Evaluation: Passing unit tests did not ensure correct gameplay behavior: manual assessment exposed incorrect runtime behavior in the car platform mechanic.Game functionality may depend on runtime interactions that code-level validation alone does not capture.
- Limitations: Generalizability is limited because the study used one custom game project, one model, and single runs without accounting for model nondeterminism.The custom codebase may introduce selection or design bias, and findings may not transfer across genres, architectures, or development contexts.
- Limitations: The refactoring-versus-gameplay difference was not a controlled benchmark comparison because tasks were not calibrated for equivalent difficulty.Evaluation used software metrics, unit tests, and manual gameplay assessments, but did not fully capture long-term quality, maintainability, or developer effort.
- Responsible use: Responsible LLM use requires balancing computational costs with benefits and combining transparent reporting, human oversight, and code-level and gameplay-level validation.Generated code may contain subtle defects despite appearing plausible.
6 Conclusion & Future Work
The exploratory case study found GPT-4o more reliable for refactoring than gameplay feature generation, while emphasizing that this pattern requires broader validation beyond the single case. Future work should test additional models, tasks, runs, projects, task-difficulty calibration, and agentic coding systems.
- Conclusion: GPT-4o handled the selected refactoring tasks more reliably than the gameplay feature generation tasks, although the pattern is limited by the exploratory single-case design.The conclusion frames the result as specific to the examined setting rather than broadly established.
- Future Work: Future studies should examine additional LLMs, larger and more diverse task sets, repeated independent model runs, and different game projects.These extensions would assess whether the observed pattern remains stable across broader development contexts.
- Future Work: Future work should calibrate task difficulty before comparing development-task classes and investigate whether agentic coding systems can reduce required human guidance in iterative workflows.The proposed investigations address comparison validity and the amount of human guidance needed.