一句话：提出可扩展的 Python 自动游戏测试工具 MIMIC-Py，将人格驱动的 LLM 智能体封装为可复用框架。
现代视频游戏是复杂且非确定的系统，大规模自动化测试困难。已有研究表明，人格驱动的大语言模型（LLM）智能体可提升行为多样性与测试覆盖率，但现有工具多为研究原型，缺乏跨游戏复用性。MIMIC-Py 是一个基于 Python 的自动化游戏测试工具，将人格驱动的 LLM 智能体转化为可复用、可扩展的框架。它把人格特质暴露为可配置输入，采用模块化架构，将规划、执行与记忆同游戏特定逻辑解耦，并支持通过公开 API 或合成代码等多种交互机制与游戏交互。作者描述了 MIMIC-Py 的设计，并展示其能以极少的工程投入部署到新游戏环境，从而弥合研究原型与实际自动化游戏测试之间的差距。
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/.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2604-07752,
title = {MIMIC-Py: An Extensible Tool for Personality-Driven Automated Game Testing with Large Language Models},
author = {Yifei Chen and Sarra Habchi and Lili Wei},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3806414}
}