MIMIC-Py: An Extensible Tool for Personality-Driven Automated Game Testing with Large Language Models
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/.
Wed 8 JulDisplayed time zone: Eastern Time (US & Canada) change
14:00 - 15:30 | LLM for SE 5Tool Demonstrations / Ideas, Visions and Reflections / Research Papers at MB 2.210 Chair(s): Banani Roy University of Saskatchewan | ||
14:00 20mTalk | Red Teaming LLMs via Linguistic-Aware Fuzzing Research Papers Shuai Yuan University of Electronic Science and Technology of China, Nian Luo University Of Electronic Science And Technology Of China, Jingling Sun University of Electronic Science and Technology of China, Yihao Huang National University of Singapore, Singapore, Chengyu Zhang Loughborough University | ||
14:20 10mTalk | MIMIC-Py: An Extensible Tool for Personality-Driven Automated Game Testing with Large Language Models Tool Demonstrations | ||
14:30 10mTalk | Towards Automated Test Adaptation in Fork Ecosystems via Large Language Models Ideas, Visions and Reflections Mukelabai Mukelabai Ruhr University Bochum, Keanu-Wesley Schurkus Ruhr University Bochum, Yannic Noller Ruhr University Bochum, Thorsten Berger Ruhr University Bochum | ||
14:40 20mTalk | Boosting LLMs for Mutation Generation Research Papers Bo Wang Beijing Jiaotong University, Ming Deng Beijing Jiaotong University, Mingda Chen Beijing Jiaotong University, Chengran Yang Singapore Management University, Singapore, Youfang Lin Beijing Jiaotong University, Mark Harman Meta Platforms, Inc. and UCL, Mike Papadakis University of Luxembourg, Jie M. Zhang Mistral AI and King's College London | ||
15:00 20mTalk | LLM-Assisted Input-Requirement-Aware Differential Testing of Array Programming Frameworks Research Papers Zhichao Zhou School of Information Science and Technology, ShanghaiTech University, Jingzhu He ShanghaiTech University Pre-print | ||
15:20 10mTalk | AISysRev - LLM-based Tool for Title-abstract Screening Tool Demonstrations Aleksi Huotala University of Helsinki, Miikka Kuutila LUT University, Olli-Pekka Turtio University of Helsinki, Simo Sipilä University of Helsinki, Mika Mäntylä University of Helsinki Pre-print | ||