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P2 Agents in the Sandbox: End-to-End Crash Bug Reproduction for Minecraft

沙箱中的智能体:Minecraft 崩溃缺陷的端到端复现
ASE 2025 40th IEEE/ACM International Conference on Automated Software Engineering (ASE)2025匹配度 3/10 · 该论文利用 LLM 与视觉 Agent 自动化 Minecraft 崩溃 bug 复现,属于 LLM/Agent 在游戏测试与调试中的应用,与用户核心关注的静态分析、浮点程序分析、贝叶斯程序分析及 LLM 与静态分析/形式化验证结合方向关联较弱;仅因涉及 Agentic 程序和测试调试标签而略有交叉。引用 1 inbox
Eray Yapağcı, Yavuz Alp Sencer Öztürk, Eray Tüzün
crossref: 2026-09-12

AI 速览

一句话:提出 BugCraft,用 LLM 将 Minecraft 崩溃报告转为复现步骤,并由视觉智能体在游戏内执行,实现端到端自动复现。

在 Minecraft 等持续演化的游戏中,崩溃 bug 复现高度依赖人工场景重建,而已有 LLM 驱动的 bug 复现研究很少覆盖复杂交互式游戏。本文提出 BugCraft:第一阶段 Step Synthesizer 结合 LLM 与 Minecraft Wiki 知识,将崩溃报告转化为结构化的复现步骤(S2R);第二阶段 Action Model 使用基于视觉的 LLM 智能体和自定义宏 API,在游戏中执行这些步骤以触发崩溃。作者构建 BugCraft-Bench 数据集进行评估,使用 GPT-4.1 端到端复现了 34.9% 的崩溃 bug,比计算机使用基线模型高 37%,表明 LLM 自动复现复杂游戏崩溃 bug 的可行性。

问题
Minecraft 等游戏中的崩溃 bug 报告通常需要人工重建场景才能复现,过程繁琐且难以自动化,现有 LLM bug 复现方法未处理复杂游戏环境。
方法
提出 BugCraft:先用 LLM 与 Minecraft Wiki 将报告合成为结构化复现步骤,再由视觉 LLM 智能体通过宏 API 在游戏内执行步骤以触发崩溃。
评估
在自建 BugCraft-Bench 崩溃报告数据集上端到端评估,GPT-4.1 复现率 34.9%,较 computer-use 基线模型提升 37%。
原文摘要(Abstract)

Reproducing game bugs, particularly crash bugs in continuously evolving games like Minecraft, is a notoriously manual, time-consuming, and challenging process to automate; insights from a key decision maker from Minecraft we interviewed confirm this, highlighting that a substantial portion of crash reports necessitate manual scenario reconstruction. Despite the success of LLM-driven bug reproduction in other software domains, games, with their complex interactive environments, remain largely unaddressed. This paper introduces BugCraft, a novel end-to-end framework designed to automate the reproduction of crash bugs in Minecraft directly from user-submitted bug reports, addressing the critical gap in automated game bug reproduction. BugCraft employs a two-stage approach: first, a Step Synthesizer leverages LLMs and Minecraft Wiki knowledge to transform bug reports into high-quality, structured steps to reproduce (S2R). Second, an Action Model, powered by a vision-based LLM agent and a custom macro API, executes these S2R steps within Minecraft to trigger the reported crash. To facilitate evaluation, we introduce BugCraft-Bench, a curated dataset of Minecraft crash bug reports. On BugCraft-Bench, our framework end-to-end reproduced 34.9% of crash bugs with GPT-4.1, outperforming baseline computer-use models by 37%. BugCraft demonstrates the feasibility of automated reproduction of crash bugs in complex game environments using LLMs, opening promising avenues for game testing and development. Finally, we make our code open at https://bugcraft2025.github.io.

链接与引用

DOI 原文 ·

BibTeX
@inproceedings{paperbot9424,
  title = {Agents in the Sandbox: End-to-End Crash Bug Reproduction for Minecraft},
  author = {Eray Yapağcı and Yavuz Alp Sencer Öztürk and Eray Tüzün},
  booktitle = {ASE},
  year = {2025},
  doi = {10.1109/ase63991.2025.00254}
}