Toward the Trustworthiness of Industrial Robotics Using Differential Fuzz Testing
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Author/Creator ORCID
Date
2022-10-13
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Citation of Original Publication
B. Wang, R. Wang and H. Song, "Toward the Trustworthiness of Industrial Robotics Using Differential Fuzz Testing," in IEEE Transactions on Industrial Informatics, 2022, doi: 10.1109/TII.2022.3211888.
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Subjects
Abstract
Intelligent robots are a current application in
Industrial Internet of Things (IIoT), with their trustworthiness being a topic of considerable research interest. Vulnerabilities in robot software may affect the trustworthiness of robotics. To detect these vulnerabilities in robot
software, this study proposes a differential fuzz testing
method. The main idea is to continuously execute test
cases for different versions of software packages to detect
inconsistencies among outputs and eventually discover
vulnerabilities. First, test cases are generated, combining
seed generation and mutation, after which the measured
model of the packages in RVIZ is built and the generated
seeds are executed. The differences among inconsistent
outputs are calculated and the causes of the differences
analyzed. Two evaluation metrics for the inconsistencies
and seeds are presented. This method is applied to the
crucial package in ROS-MoveIt!. The results show that the
arm.go() of moveit commander has joint angle overflow.