Fuzz Testing in Behavior-Based Robotics

Rodrigo Delgado, Miguel Campusano*, Alexandre Bergel

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

The behavior of a robot is typically expressed as a set of source code files written using a programming language. As for any software engineering activity, programming robotic behaviors is a complex and error-prone task. This paper propose a methodology that aims to reduce the cost of producing a reliable software describing a robotic behavior by automatically testing it. We employ a fuzz testing technique to stress software components with randomly generated data. By applying fuzz testing to a complex robotic-software, we identified errors related to the coding, the way data is handled, the logic of the robotic behavior, and the initialization of architectural components. Furthermore, a panel of experts acquainted with the analyzed behavior have highlighted the relevance and the significance of our findings. Our fuzzer operates on the SMACH and ROS frameworks and it is available under the MIT public open source license.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Robotics and Automation, ICRA 2021
PublisherIEEE
Publication date2021
Pages9375-9381
ISBN (Electronic)9781728190778
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Robotics and Automation, ICRA 2021 - Xi'an, China
Duration: 30. May 20215. Jun 2021

Conference

Conference2021 IEEE International Conference on Robotics and Automation, ICRA 2021
Country/TerritoryChina
CityXi'an
Period30/05/202105/06/2021
SponsorBaidu, Biomimetic Intelligence and Robotics, DJI, et al., Mech Mind Robotics Technologies, Toyota Research Institute
Series IEEE International Conference on Robotics and Automation
Volume2021-May
ISSN2152-4092

Bibliographical note

Funding Information:
The work presented in this paper was partially funded by Fondecyt Regular 1200067, Lam Research, and by the Innovation Fund Denmark project HealthDrone.

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