Knowledge Graph-enhanced Large Language Model for Incremental Game PlayTesting

TL;DR

This paper introduces KLPEG, a framework combining knowledge graphs and large language models to improve incremental game playtesting by efficiently identifying affected functionalities and generating tailored test cases.

Contribution

The paper presents a novel framework that integrates knowledge graphs with LLMs for structured knowledge accumulation and targeted testing in incremental game updates.

Findings

More accurate localization of affected functionalities

Fewer steps needed to complete tests

Enhanced testing effectiveness and efficiency

Abstract

The rapid iteration and frequent updates of modern video games pose significant challenges to the efficiency and specificity of testing. Although automated playtesting methods based on Large Language Models (LLMs) have shown promise, they often lack structured knowledge accumulation mechanisms, making it difficult to conduct precise and efficient testing tailored for incremental game updates. To address this challenge, this paper proposes a KLPEG framework. The framework constructs and maintains a Knowledge Graph (KG) to systematically model game elements, task dependencies, and causal relationships, enabling knowledge accumulation and reuse across versions. Building on this foundation, the framework utilizes LLMs to parse natural language update logs, identify the scope of impact through multi-hop reasoning on the KG, enabling the generation of update-tailored test cases. Experiments in two representative game environments, Overcooked and Minecraft, demonstrate that KLPEG can more accurately locate functionalities affected by updates and complete tests in fewer steps, significantly improving both playtesting effectiveness and efficiency.