Knowledge Graph-enhanced Large Language Model for Incremental Game PlayTesting
Enhong Mu, Jinyu Cai, Yijun Lu, Mingyue Zhang, Kenji Tei, Jialong Li
TL;DR
This paper tackles the challenge of efficient playtesting under frequent incremental game updates by introducing KLPEG, a knowledge graph–driven framework that accumulates and reuses cross-version knowledge. KLPEG uses a knowledge graph to model game elements, dependencies, and causal relations, and employs LLMs to parse update logs and perform multi-hop reasoning to localize impact. The approach enables generation of update-tailored test cases and demonstrates improved accuracy and efficiency in two games, Overcooked and Minecraft. This framework supports agile development by reducing testing steps and time and by enabling knowledge transfer across versions.
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.