MIMIC-Py: An Extensible Tool for Personality-Driven Automated Game Testing with Large Language Models
This tool addresses the problem of practical automated testing for complex video games, though it is incremental as it builds on prior research prototypes.
The authors tackled the challenge of automating game testing at scale by developing MIMIC-Py, a Python-based tool that uses personality-driven LLM agents to improve behavioral diversity and test coverage, enabling deployment to new game environments with minimal engineering effort.
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/.