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P2 Metamorphic Fuzzing for Multi-Agent Path Finding Algorithms

多智能体路径规划算法的蜕变模糊测试
ICSE Proceedings of the 2026 IEEE/ACM 48th International Conference on Software Engineering2026匹配度 1/10 · 该论文研究多智能体路径规划求解器的蜕变模糊测试，属于传统软件测试与搜索算法领域；与用户的静态分析、LLM/Agentic 程序分析、浮点程序分析、贝叶斯程序分析等方向几乎无交集，且文中的“多智能体”并非 LLM Agent。引用 0 inbox
Luxia Lin, Xudong Zhang, Shihao Zhu, Yan Cai
测试、调试与修复
crossref: 2026-09-12

AI 速览

一句话：提出蜕变模糊测试方法，系统检测多智能体路径规划求解器的完备性与解质量缺陷。

多智能体路径规划（MAPF）在仓储物流、机器人及自动驾驶中应用广泛，确保求解器在解存在时总能返回解并生成高质量路径至关重要。然而MAPF算法测试不足，地图拓扑与智能体配置构成的高维紧耦合输入空间使系统化测试极具挑战。本文提出蜕变模糊测试方法，利用蜕变关系生成测试用例，以检测求解器的完备性与解质量缺陷。摘要未给出具体实验结果。

问题
MAPF算法缺乏充分测试，高维紧耦合输入空间导致系统化测试困难，需保证求解器完备性与解质量。
方法
采用蜕变模糊测试，通过定义蜕变关系生成测试用例并检测MAPF求解器缺陷。
评估
摘要未提供评估细节，无法概括具体评估方式。
原文摘要（Abstract）

Multi-Agent Path Finding (MAPF) is a fundamental problem in multi-agent systems, with broad applications in warehouse logistics, robotics, and autonomous driving. Ensuring that MAPF solvers consistently return a solution whenever one exists and deliver high-quality plans is critical for real-world deployment. However, MAPF algorithms remain insufficiently tested. The high-dimensional, tightly coupled input space arising from map topology and agent configurations makes systematic testing extremely challenging.

链接与引用

DOI 原文 · PDF（开放获取） ·

BibTeX 
@inproceedings{paperbot11731,
  title = {Metamorphic Fuzzing for Multi-Agent Path Finding Algorithms},
  author = {Luxia Lin and Xudong Zhang and Shihao Zhu and Yan Cai},
  booktitle = {ICSE},
  year = {2026},
  doi = {10.1145/3744916.3787841}
}