用知识图谱增强大模型，让游戏测试随更新自动精准定位问题。
Knowledge Graph-enhanced Large Language Model for Incremental Game PlayTesting
- 构建游戏知识图谱，系统化存储元素与依赖关系
- 通过多跳推理定位更新影响范围，测试步骤减少30%以上
- 适合需要频繁迭代的独立游戏或开源项目团队
现代视频游戏迭代迅速，频繁更新给测试效率和针对性带来挑战。尽管基于大语言模型（LLM）的自动化测试已有进展，但普遍缺乏结构化知识积累机制，难以针对增量更新进行精准高效测试。为此，本文提出KLPEG框架：通过构建并维护知识图谱（KG），系统建模游戏元素、任务依赖与因果关系，实现跨版本的知识累积与复用。在此基础上，利用LLM解析自然语言更新日志，结合KG进行多跳推理，精准识别更新影响范围，生成适配新版本的测试用例。在Overcooked和Minecraft两个典型游戏环境中实验表明，KLPEG能更准确地定位受影响功能，以更少步骤完成测试，显著提升测试效果与效率。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。