一句话：提出基于行为树的反馈机制，提升 ROS 程序模糊测试器对机器人状态的区分能力与独特崩溃检测效果。
机器人越来越多承担关键任务，但 ROS 程序仍常含可导致严重故障的软件漏洞。已有 ROS 程序模糊测试器虽能自动检测漏洞，却缺乏适配 ROS 特性的反馈机制。例如最新工具 ROFER 无法正确区分多个不同的机器人状态，又会把相同状态误判为不同状态，因而难以有效发现独特崩溃。为此，论文提出 BTreeFuzz，其核心是基于行为树来增强模糊测试的反馈机制，使反馈度量能更好地反映机器人程序的状态与行为差异，从而指导测试用例生成并提高崩溃检测能力。摘要未给出具体实验数据，但目标是解决现有反馈机制对状态区分不准确的问题。
Robots are increasingly taking on human roles and functions, and societal expectations for these machines to perform critical tasks and eliminate the problem of human error continue to rise. The robot operating system (ROS) is considered the de facto standard that has been widely used to develop robotic programs. However, even though robot malfunctions should be minimized, it remains highly challenging to develop ROS programs without software vulnerabilities. As a result, many ROS programs still contain software vulnerabilities that can lead to serious malfunctions. To address this concern, researchers have recently presented several fuzzing techniques to automatically detect these vulnerabilities in ROS programs. A feedback mechanism for fuzzing requires a specific metric tailored to the features of ROS programs. However, existing ROS program fuzzers have been unable to provide adequate feedback mechanisms to make this possible. For instance, ROFER, a state-of-the-art ROS program fuzzer, cannot correctly differentiate between multiple distinct robot statuses based on its feedback mechanism metric, and often mistakenly identifies identical statuses as being different from each other, making it ineffective at detecting unique crashes.
@inproceedings{paperbot11526,
title = {BTreeFuzz: Enhanced Feedback Mechanism for ROS Program Fuzzer Based on Behavior Tree},
author = {Hee Yeon Kim and Gyunghoon Kim and Dong Hoon Lee and Wonsuk Choi},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3744916.3773261}
}