用大模型生成有性格的游戏测试员，跨游戏复用无需重写代码。
MIMIC-Py: An Extensible Tool for Personality-Driven Automated Game Testing with Large Language Models
- 将性格特征设为可调参数，用模块化设计分离规划与游戏逻辑。
- 支持通过API或自动生成代码与多款游戏交互，部署新游戏仅需少量修改。
- 适合游戏测试工程师和自动化工具开发者快速构建测试框架。
现代视频游戏是复杂且非确定性的系统，难以规模化自动测试。尽管已有研究证明性格驱动的大型语言模型（LLM）代理能提升行为多样性和测试覆盖率，但现有工具多为研究原型，缺乏跨游戏复用性。本文介绍MIMIC-Py，一个基于Python的自动化游戏测试工具，将性格驱动的LLM代理转化为可复用、可扩展的框架。MIMIC-Py将性格特质作为可配置输入，并采用模块化架构，分离规划、执行与记忆模块与游戏特定逻辑。它支持多种交互方式，使代理可通过暴露的API或合成代码与游戏交互。本文描述了MIMIC-Py的设计，并展示其在新游戏环境中的快速部署能力，显著缩小研究原型与实际应用之间的差距。源代码及演示视频详见项目主页：https://mimic-persona.github.io/MIMIC-Py-Home-Page/。
原文摘要 · Abstract (English)
Modern video games are complex, non-deterministic systems that are difficult to test automatically at scale. Although prior work shows that personality-driven Large Language Model (LLM) agents can improve behavioural diversity and test coverage, existing tools largely remain research prototypes and lack cross-game reusability. This tool paper presents MIMIC-Py, a Python-based automated game-testing tool that transforms personality-driven LLM agents into a reusable and extensible framework. MIMIC-Py exposes personality traits as configurable inputs and adopts a modular architecture that decouples planning, execution, and memory from game-specific logic. It supports multiple interaction mechanisms, enabling agents to interact with games via exposed APIs or synthesized code. We describe the design of MIMIC-Py and show how it enables deployment to new game environments with minimal engineering effort, bridging the gap between research prototypes and practical automated game testing. The source code and a demo video are available on our project webpage: https://mimic-persona.github.io/MIMIC-Py-Home-Page/.
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