Reinforcement Learning-Based Fuzz Testing for the Gazebo Robotic Simulator

Gazebo, being the most widely utilized simulator in robotics, plays a pivotal role in developing and testing robotic systems. Given its impact on the safety and reliability of robotic operations, early bug detection is critical. However, due to the challenges of strict input structures and vast stat...

Full description

Saved in:

Bibliographic Details
Published in:	Proceedings of the ACM on software engineering Vol. 2; no. ISSTA; pp. 1467 - 1488
Main Authors:	Ren, Zhilei, Li, Yitao, Li, Xiaochen, Qi, Guanxiao, Xuan, Jifeng, Jiang, He
Format:	Journal Article
Language:	English
Published:	New York, NY, USA ACM 22.06.2025
Subjects:	Software and its engineering Software testing and debugging Fuzz Testing Gazebo Robotic Simulator Software Testing
ISSN:	2994-970X, 2994-970X
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Gazebo, being the most widely utilized simulator in robotics, plays a pivotal role in developing and testing robotic systems. Given its impact on the safety and reliability of robotic operations, early bug detection is critical. However, due to the challenges of strict input structures and vast state space, it is not effective to directly use existing fuzz testing approach to Gazebo. In this paper, we present GzFuzz, the first fuzz testing framework designed for Gazebo. GzFuzz addresses these challenges through a syntax-aware feasible command generation mechanism to handle strict input requirements, and a reinforcement learning-based command generator selection mechanism to efficiently explore the state space. By combining the two mechanisms under a unified framework, GzFuzz is able to detect bugs in Gazebo effectively. In extensive experiments, GzFuzz is able to detect an average of 9.6 unique bugs in 12 hours, and exhibits a substantial increase in code coverage than existing fuzzers AFL++ and Fuzzotron, with a proportionate improvement of approximately 239%-363%. In less than six months, GzFuzz uncovered 25 unique crashes in Gazebo, 24 of which have been fixed or confirmed. Our results highlight the importance of directly fuzzing Gazebo, thereby presenting a novel and potent methodology that serves as an inspiration for enhancing testing across a broader range of simulators.
AbstractList	Gazebo, being the most widely utilized simulator in robotics, plays a pivotal role in developing and testing robotic systems. Given its impact on the safety and reliability of robotic operations, early bug detection is critical. However, due to the challenges of strict input structures and vast state space, it is not effective to directly use existing fuzz testing approach to Gazebo. In this paper, we present GzFuzz, the first fuzz testing framework designed for Gazebo. GzFuzz addresses these challenges through a syntax-aware feasible command generation mechanism to handle strict input requirements, and a reinforcement learning-based command generator selection mechanism to efficiently explore the state space. By combining the two mechanisms under a unified framework, GzFuzz is able to detect bugs in Gazebo effectively. In extensive experiments, GzFuzz is able to detect an average of 9.6 unique bugs in 12 hours, and exhibits a substantial increase in code coverage than existing fuzzers AFL++ and Fuzzotron, with a proportionate improvement of approximately 239%-363%. In less than six months, GzFuzz uncovered 25 unique crashes in Gazebo, 24 of which have been fixed or confirmed. Our results highlight the importance of directly fuzzing Gazebo, thereby presenting a novel and potent methodology that serves as an inspiration for enhancing testing across a broader range of simulators. Gazebo, being the most widely utilized simulator in robotics, plays a pivotal role in developing and testing robotic systems. Given its impact on the safety and reliability of robotic operations, early bug detection is critical. However, due to the challenges of strict input structures and vast state space, it is not effective to directly use existing fuzz testing approach to Gazebo. In this paper, we present GzFuzz, the first fuzz testing framework designed for Gazebo. GzFuzz addresses these challenges through a syntax-aware feasible command generation mechanism to handle strict input requirements, and a reinforcement learning-based command generator selection mechanism to efficiently explore the state space. By combining the two mechanisms under a unified framework, GzFuzz is able to detect bugs in Gazebo effectively. In extensive experiments, GzFuzz is able to detect an average of 9.6 unique bugs in 12 hours, and exhibits a substantial increase in code coverage than existing fuzzers AFL++ and Fuzzotron, with a proportionate improvement of approximately 239%-363%. In less than six months, GzFuzz uncovered 25 unique crashes in Gazebo, 24 of which have been fixed or confirmed. Our results highlight the importance of directly fuzzing Gazebo, thereby presenting a novel and potent methodology that serves as an inspiration for enhancing testing across a broader range of simulators.
ArticleNumber	ISSTA065
Author	Li, Yitao Li, Xiaochen Ren, Zhilei Jiang, He Qi, Guanxiao Xuan, Jifeng
Author_xml	– sequence: 1 givenname: Zhilei orcidid: 0000-0002-1511-2158 surname: Ren fullname: Ren, Zhilei email: zren@dlut.edu.cn organization: Dalian University of Technology, Dalian, China – sequence: 2 givenname: Yitao orcidid: 0009-0004-0832-9406 surname: Li fullname: Li, Yitao email: liyitao@mail.dlut.edu.cn organization: Dalian University of Technology, Dalian, China – sequence: 3 givenname: Xiaochen orcidid: 0000-0002-5068-1938 surname: Li fullname: Li, Xiaochen email: xiaochen.li@dlut.edu.cn organization: Dalian University of Technology, Dalian, China – sequence: 4 givenname: Guanxiao orcidid: 0009-0007-6838-6308 surname: Qi fullname: Qi, Guanxiao email: gxqi@mail.dlut.edu.cn organization: Dalian University of Technology, Dalian, China – sequence: 5 givenname: Jifeng orcidid: 0000-0002-2968-3496 surname: Xuan fullname: Xuan, Jifeng email: jxuan@whu.edu.cn organization: Wuhan University, Wuhan, China – sequence: 6 givenname: He orcidid: 0000-0001-8674-4948 surname: Jiang fullname: Jiang, He email: jianghe@dlut.edu.cn organization: Dalian University of Technology, Dalian, China
BookMark	eNpNkDFPwzAUhC1UJEqp2Jm8MQVsx4ntESpaQJGQSga2yHafIaixkZ0O5Nc3qAUx3enu0w13jiY-eEDokpIbSnlxmwsmFWcnaMqU4pkS5G3yz5-heUqfhJAxoVSQKXpeQ-tdiBY68D2uQEff-vfsXifY4OVuGHANqR8jPFK4_wC80gOYgNfBhL61-LXtdlvdh3iBTp3eJpgfdYbq5UO9eMyql9XT4q7KdEFZVhrtRK6VYJaUgpqyYGUBwhJHHDeMMFpw4oRzYK3JJagcNnzDJEgpRQk8n6Hrw6yNIaUIrvmKbafjd0NJ83NCczxhJK8OpLbdH_Rb7gGLlFg6
Cites_doi	10.1145/3616394.3618266 10.5772/5618 10.1016/j.infsof.2021.106625 10.1145/3540250.3549164 10.1016/j.jss.2022.111574 10.1073/pnas.1907856118 10.1109/NetSoft57336.2023.10175496 10.14722/ndss.2024.24556 10.1007/s11432-023-4127-5 10.1109/TCE.2023.3269528 10.1109/ICST46399.2020.00062 10.1146/annurev-control-062722-100728 10.1007/s10664-022-10233-3 10.1109/TRO.2023.3323938 10.1016/j.jss.2024.111963 10.1145/3597926.3598052 10.1109/TR.2018.2850315 10.1109/TASE.2014.2368997 10.1145/3324884.3416570 10.1109/ICST46399.2020.00020 10.1145/3533767.3534376
ContentType	Journal Article
Copyright	Copyright is held by the owner/author(s). Publication rights licensed to ACM.
Copyright_xml	– notice: Copyright is held by the owner/author(s). Publication rights licensed to ACM.
DBID	AAYXX CITATION
DOI	10.1145/3728942
DatabaseName	CrossRef
DatabaseTitle	CrossRef
DatabaseTitleList	CrossRef
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	2994-970X
EndPage	1488
ExternalDocumentID	10_1145_3728942 3728942
GrantInformation_xml	– fundername: Foundational Research Funds for the Central Universities grantid: DUT24LAB126 – fundername: National Natural Science Foundation of China grantid: 62132020, 62032004, 62432010, 62072068 funderid: https://doi.org/10.13039/501100001809 – fundername: National Key Research and Development Program of China grantid: 2018YF-B1003900 funderid: https://doi.org/10.13039/501100012166
GroupedDBID	AAKMM ACM AEJOY AKRVB ALMA_UNASSIGNED_HOLDINGS LHSKQ M~E AAYXX CITATION
ID	FETCH-LOGICAL-a512-6baf73a972c0671b65265e7c0f0f4b2021540f7ffeccb38e93ed4d28e88876e43
ISSN	2994-970X
IngestDate	Sat Nov 29 07:43:49 EST 2025 Mon Jul 14 20:48:59 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	ISSTA
Keywords	Fuzz Testing Gazebo Robotic Simulator Software Testing
Language	English
License	Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-a512-6baf73a972c0671b65265e7c0f0f4b2021540f7ffeccb38e93ed4d28e88876e43
ORCID	0000-0002-5068-1938 0000-0002-1511-2158 0000-0002-2968-3496 0000-0001-8674-4948 0009-0007-6838-6308 0009-0004-0832-9406
OpenAccessLink	https://dl.acm.org/doi/10.1145/3728942
PageCount	22
ParticipantIDs	crossref_primary_10_1145_3728942 acm_primary_3728942
PublicationCentury	2000
PublicationDate	20250622 2025-06-22
PublicationDateYYYYMMDD	2025-06-22
PublicationDate_xml	– month: 06 year: 2025 text: 20250622 day: 22
PublicationDecade	2020
PublicationPlace	New York, NY, USA
PublicationPlace_xml	– name: New York, NY, USA
PublicationTitle	Proceedings of the ACM on software engineering
PublicationTitleAbbrev	ACM PACMSE
PublicationYear	2025
Publisher	ACM
Publisher_xml	– name: ACM
References	Andrea Fioraldi, Dominik Maier, Heiko Eiß feldt, and Marc Heuse. 2020. AFL++: Combining Incremental Steps of Fuzzing Research. In 14th USENIX Workshop on Offensive Technologies (WOOT). Chenyuan Yang, Yinlin Deng, Runyu Lu, Jiayi Yao, Jiawei Liu, Reyhaneh Jabbarvand, and Lingming Zhang. 2024. WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models. Proc. ACM Program. Lang., 8, OOPSLA2 (2024), Article 296, Oct., 27 pages. Van-Thuan Pham, Marcel Böhme, and Abhik Roychoudhury. 2020. AFLNet: A Greybox Fuzzer for Network Protocols. In Proceedings of the 13rd IEEE International Conference on Software Testing, Verification and Validation : Testing Tools Track. Afsoon Afzal, Deborah S Katz, Claire Le Goues, and Christopher S Timperley. 2021. Simulation for Robotics Test Automation: Developer Perspectives. In 2021 14th IEEE Conference on Software Testing, Verification and Validation (ICST). 263–274. Muhammad Ali Gulzar, Yongkang Zhu, and Xiaofeng Han. 2019. Perception and Practices of Differential Testing. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). 71–80. Yuting Chen, Ting Su, Chengnian Sun, Zhendong Su, and Jianjun Zhao. 2016. Coverage-Directed Differential Testing of JVM Implementations. In proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI). 85–99. Alexander Smirnov and Nikolay Teslya. 2020. Modeling of Robot Interaction in Coalition Through Smart Space and Blockchain: Precision Agriculture Scenario. In International Conference on Enterprise Information Systems (ICEIS). 481–497. Mohamed Fasil Syed Ahamed, Girma Tewolde, and Jaerock Kwon. 2018. Software-in-the-Loop Modeling and Simulation Framework for Autonomous Vehicles. In 2018 IEEE International Conference on Electro/Information Technology (EIT). 0305–0310. Megan Olsen and Mohammad Raunak. 2019. Increasing Validity of Simulation Models Through Metamorphic Testing. IEEE Transactions on Reliability, 68, 1 (2019), 91–108. Danielle Dauphinais, Michael Zylka, Harris Spahic, Farhan Shaik, Jingda Yang, Isabella Cruz, Jakob Gibson, and Ying Wang. 2023. Automated Vulnerability Testing and Detection Digital Twin Framework for 5G Systems. In 2023 IEEE 9th International Conference on Network Softwarization (NetSoft). 308–310. Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). Michel Nass, Emil Alégroth, and Robert Feldt. 2021. Why Many Challenges with GUI Test Automation (Will) Remain. Information and Software Technology, 138 (2021), 106625. Junjie Chen, Chenyao Suo, Jiajun Jiang, Peiqi Chen, and Xingjian Li. 2023. Compiler Test-Program Generation via Memoized Configuration Search. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE). 2035–2047. Dongwei Xiao, Zhibo Liu, and Shuai Wang. 2023. PhyFu: Fuzzing Modern Physics Simulation Engines. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE). 1579–1591. Mayank Sharma, Pingshi Yu, and Alastair F Donaldson. 2023. Rustsmith: Random Differential Compiler Testing for Rust. In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA). 1483–1486. Chang-Ai Sun, An Fu, Pak-Lok Poon, Xiaoyuan Xie, Huai Liu, and Tsong Yueh Chen. 2021. METRIC^++: A Metamorphic Relation Identification Technique Based on Input Plus Output Domains. IEEE Transactions on Software Engineering, 47, 9 (2021), 1764–1785. Patrick E McKnight and Julius Najab. 2010. Mann-Whitney U Test. The Corsini Encyclopedia of Psychology, 1–1. Olivier Michel. 2004. Cyberbotics ltd. Webots™ : Professional Mobile Robot Simulation. International Journal of Advanced Robotic Systems, 1, 1 (2004), 5. Junjie Chen, Haoyang Ma, and Lingming Zhang. 2020. Enhanced Compiler Bug Isolation via Memoized Search. In Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering (ASE). 78–89. Kamak Ebadi, Lukas Bernreiter, Harel Biggie, Gavin Catt, Yun Chang, Arghya Chatterjee, Christopher E Denniston, Simon-Pierre Deschênes, Kyle Harlow, and Shehryar Khattak. 2024. Present and Future of SLAM in Extreme Environments: The DARPA SubT Challenge. IEEE Transactions on Robotics, 40 (2024), 936–959. Ruyi Zhou, Wenhao Feng, Liang Ding, Huaiguang Yang, Haibo Gao, Guangjun Liu, and Zongquan Deng. 2022. MarsSim: a High-Fidelity Physical and Visual Simulation for Mars Rovers. IEEE Trans. Aerospace Electron. Systems, 59, 2 (2022), 1879–1892. Ruijie Meng, Martin Mirchev, Marcel Böhme, and Abhik Roychoudhury. 2024. Large Language Model Guided Protocol Fuzzing. In Proceedings of the 31st Annual Network and Distributed System Security Symposium (NDSS). Xiaogang Zhu, Sheng Wen, Seyit Camtepe, and Yang Xiang. 2022. Fuzzing: A Survey for Roadmap. ACM Comput. Surv., 54, 11s (2022), Article 230, Sept., 36 pages. issn:0360-0300 Roberto Natella. 2022. StateAFL: Greybox Fuzzing for Stateful Network Servers. Empirical Software Engineering, 27, 7 (2022), 191. Yinlin Deng, Chunqiu Steven Xia, Chenyuan Yang, Shizhuo Dylan Zhang, Shujing Yang, and Lingming Zhang. 2024. Large Language Models are Edge-Case Generators: Crafting Unusual Programs for Fuzzing Deep Learning Libraries. In Proceedings of the 46th IEEE/ACM International Conference on Software Engineering (ICSE). ACM, 70:1–70:13. Sergio García, Daniel Strüber, Davide Brugali, Thorsten Berger, and Patrizio Pelliccione. 2020. Robotics Software Engineering: A Perspective from the Service Robotics Domain. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE). 593–604. Muhammad Numair Mansur, Valentin Wüstholz, and Maria Christakis. 2023. Dependency-Aware Metamorphic Testing of Datalog Engines. In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA). ACM, 236–247. Nathan Koenig and Andrew Howard. 2004. Design and Use Paradigms for Gazebo, an Open-Source Multi-Robot Simulator. In 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 3, 2149–2154. Philippe Bardou, Jérôme Mariette, Frédéric Escudié, Christophe Djemiel, and Christophe Klopp. 2014. JVenn: an Interactive Venn Diagram Viewer. BMC bioinformatics, 15 (2014), 1–7. Ahmed AbdelHamed, Girma Tewolde, and Jaerock Kwon. 2020. Simulation Framework for Development and Testing of Autonomous Vehicles. In 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS). 1–6. Pierre-Luc Bacon, Jean Harb, and Doina Precup. 2017. The Option-Critic Architecture. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI), Satinder Singh and Shaul Markovitch (Eds.). AAAI Press, 1726–1734. Thomas Wetzlmaier, Rudolf Ramler, and Werner Putschögl. 2016. A Framework for Monkey GUI Testing. In 2016 IEEE International Conference on Software Testing, Verification and Validation (ICST). 416–423. Yang Hong and Jun Wu. 2024. Fuzzing Digital Twin With Graphical Visualization of Electronic AVs Provable Test for Consumer Safety. IEEE Transactions on Consumer Electronics, 70, 1 (2024), 4633–4644. Anastasios Andronidis and Cristian Cadar. 2022. Snapfuzz: High-Throughput Fuzzing of Network Applications. In Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA). 340–351. Yong-Hao Zou, Jia-Ju Bai, Jielong Zhou, Jianfeng Tan, Chenggang Qin, and Shi-Min Hu. 2021. TCP-Fuzz: Detecting Memory and Semantic Bugs in TCP Stacks with Fuzzing. In 2021 USENIX Annual Technical Conference (USENIX ATC). 489–502. Michel Albonico, Milica Đ orđ ević, Engel Hamer, and Ivano Malavolta. 2023. Software Engineering Research on the Robot Operating System: A Systematic Mapping Study. Journal of Systems and Software, 197 (2023), 111574. Xiaohui Wan, Tiancheng Li, Weibin Lin, Yi Cai, and Zheng Zheng. 2024. Coverage-Guided Fuzzing for Deep Reinforcement Learning Systems. Journal of Systems and Software, 210 (2024), 111963. issn:0164-1212 HeeSun Choi, Cindy Crump, Christian Duriez, Asher Elmquist, Gregory Hager, David Han, Frank Hearl, Jessica Hodgins, Abhinandan Jain, and Frederick Leve. 2021. On the Use of Simulation in Robotics: Opportunities, Challenges, and Suggestions for Moving Forward. Proceedings of the National Academy of Sciences, 118, 1 (2021), e1907856118. Tsong Yueh Chen, Fei-Ching Kuo, Huai Liu, Pak-Lok Poon, Dave Towey, T. H. Tse, and Zhi Quan Zhou. 2018. Metamorphic Testing: A Review of Challenges and Opportunities. ACM Comput. Surv., 51, 1 (2018), 4:1–4:27. Brian Bingham, Carlos Agüero, Michael McCarrin, Joseph Klamo, Joshua Malia, Kevin Allen, Tyler Lum, Marshall Rawson, and Rumman Waqar. 2019. Toward Maritime Robotic Simulation in Gazebo. In OCEANS 2019 MTS/IEEE SEATTLE. 1–10. Jia-Ju Bai, Hao-Xuan Song, and Shi-Min Hu. 2024. Multi-Dimensional and Message-Guided Fuzzing for Robotic Programs in Robot Operating System. In Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). 763–778. Timothy H Chung, Viktor Orekhov, and Angela Maio. 2023. Into the Robotic Depths: Analysis and Insights from the DARPA Subterranean Challenge. Annual Review of Control, Robotics, and Autonomous Systems, 6, 1 (2023), 477–502. Marcela G Dos Santos, Bianca M Napoleão, Fabio Petrillo, Darine Ameyed, and Fehmi Jaafar. 2020. A Preliminary Systematic Mapping on Software Engineering for Robotic Systems: A Software Quality Perspective. In Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops. 647–654. Steven Macenski, Tully Foote, Brian Gerkey, Chris Lalancette, and William Woodall. 2022. Robot Operating System 2: Design, architecture, and uses in the wild. Science Robotics, 7, 66 (202 Macenski Steven (e_1_2_1_36_1) 2022 Xiao Dongwei (e_1_2_1_58_1) 2023 Rohmer Eric (e_1_2_1_50_1) 2013 Sharma Mayank (e_1_2_1_51_1) 2023 Koenig Nathan (e_1_2_1_35_1) 2004 Smirnov Alexander (e_1_2_1_52_1) 2020 Bardou Philippe (e_1_2_1_12_1) 2014 García Sergio (e_1_2_1_28_1) 2020 Sopegno L (e_1_2_1_53_1) 2022 Chen Yuting (e_1_2_1_19_1) 2016 e_1_2_1_45_1 e_1_2_1_22_1 e_1_2_1_43_1 Chung Timothy H (e_1_2_1_21_1) 2023; 6 e_1_2_1_49_1 AbdelHamed Ahmed (e_1_2_1_1_1) 2020 Bai Jia-Ju (e_1_2_1_11_1) 2024 Deng Yinlin (e_1_2_1_23_1) 2024 Michel Olivier (e_1_2_1_41_1) 2004; 1 Chen Junjie (e_1_2_1_16_1) 2023 Johnston Janis E (e_1_2_1_13_1) 2018 e_1_2_1_4_1 e_1_2_1_33_1 e_1_2_1_2_1 Sun Chang-Ai (e_1_2_1_54_1) 2021; 47 Afzal Afsoon (e_1_2_1_3_1) 2021 Diederik (e_1_2_1_34_1) 2015 Gulzar Muhammad Ali (e_1_2_1_30_1) 2019 e_1_2_1_18_1 Xie Kai-Tao (e_1_2_1_59_1) 2022 Dos Santos Marcela G (e_1_2_1_24_1) 2020 Wetzlmaier Thomas (e_1_2_1_56_1) 2016 Paszke Adam (e_1_2_1_47_1) 2019 Syed Ahamed Mohamed Fasil (e_1_2_1_6_1) 2018 e_1_2_1_42_1 Allan Mark (e_1_2_1_8_1) 2019 e_1_2_1_40_1 Bacon Pierre-Luc (e_1_2_1_10_1) 2017 Bingham Brian (e_1_2_1_14_1) 2019 Nogueira Lucas (e_1_2_1_44_1) 2014; 2014 Yang Chenyuan (e_1_2_1_60_1) 2024 Hambuchen Kimberly A (e_1_2_1_31_1) 2017 e_1_2_1_25_1 Echeverria Gilberto (e_1_2_1_26_1) 2011 Paduraru Ciprian (e_1_2_1_46_1) 2023 Xia Chunqiu Steven (e_1_2_1_57_1) 2024 Pateria Shubham (e_1_2_1_48_1) 2022 Güler Emre (e_1_2_1_29_1) 2024 Zhou Ruyi (e_1_2_1_61_1) 2022; 59 e_1_2_1_7_1 Chen Tsong Yueh (e_1_2_1_17_1) 2018; 51 e_1_2_1_55_1 e_1_2_1_5_1 Choi HeeSun (e_1_2_1_20_1) 2021; 118 Fioraldi Andrea (e_1_2_1_27_1) 2020 McKnight Patrick E (e_1_2_1_39_1) e_1_2_1_32_1 Zou Yong-Hao (e_1_2_1_63_1) 2021 e_1_2_1_38_1 e_1_2_1_15_1 Marcusso Manhães Musa Morena (e_1_2_1_37_1) 2016 e_1_2_1_9_1 Zhu Xiaogang (e_1_2_1_62_1) 2022
References_xml	– reference: Ciprian Paduraru., Rares Cristea., and Alin Stefanescu.. 2023. Automatic Fuzz Testing and Tuning Tools for Software Blueprints. In Proceedings of the 18th International Conference on Software Technologies (ICSOFT). SciTePress, 151–162. – reference: Yang Hong and Jun Wu. 2024. Fuzzing Digital Twin With Graphical Visualization of Electronic AVs Provable Test for Consumer Safety. IEEE Transactions on Consumer Electronics, 70, 1 (2024), 4633–4644. – reference: Haitham Y. Adarbah, Mehdi Sookhak, and Mohammed Atiquzzaman. 2023. A Digital Twin Environment for 5G Vehicle-to-Everything: Architecture and Open Issues. In Proceedings of the International ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks, Montreal, Mónica Aguilar-Igartua, Luis J. de la Cruz Llopis, and Thomas Begin (Eds.). ACM, 115–122. – reference: Van-Thuan Pham, Marcel Böhme, and Abhik Roychoudhury. 2020. AFLNet: A Greybox Fuzzer for Network Protocols. In Proceedings of the 13rd IEEE International Conference on Software Testing, Verification and Validation : Testing Tools Track. – reference: Emre Güler, Sergej Schumilo, Moritz Schloegel, Nils Bars, Philipp Görz, Xinyi Xu, Cemal Kaygusuz, and Thorsten Holz. 2024. Atropos: Effective Fuzzing of Web Applications for Server-Side Vulnerabilities. In USENIX Security Symposium. – reference: Steven Macenski, Tully Foote, Brian Gerkey, Chris Lalancette, and William Woodall. 2022. Robot Operating System 2: Design, architecture, and uses in the wild. Science Robotics, 7, 66 (2022), eabm6074. – reference: Timothy H Chung, Viktor Orekhov, and Angela Maio. 2023. Into the Robotic Depths: Analysis and Insights from the DARPA Subterranean Challenge. Annual Review of Control, Robotics, and Autonomous Systems, 6, 1 (2023), 477–502. – reference: Roberto Natella. 2022. StateAFL: Greybox Fuzzing for Stateful Network Servers. Empirical Software Engineering, 27, 7 (2022), 191. – reference: Kai-Tao Xie, Jia-Ju Bai, Yong-Hao Zou, and Yu-Ping Wang. 2022. ROZZ: Property-Based Fuzzing for Robotic Programs in ROS. In 2022 International Conference on Robotics and Automation (ICRA). 6786–6792. – reference: Jia-Ju Bai, Hao-Xuan Song, and Shi-Min Hu. 2024. Multi-Dimensional and Message-Guided Fuzzing for Robotic Programs in Robot Operating System. In Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). 763–778. – reference: Brian Bingham, Carlos Agüero, Michael McCarrin, Joseph Klamo, Joshua Malia, Kevin Allen, Tyler Lum, Marshall Rawson, and Rumman Waqar. 2019. Toward Maritime Robotic Simulation in Gazebo. In OCEANS 2019 MTS/IEEE SEATTLE. 1–10. – reference: Alexander Smirnov and Nikolay Teslya. 2020. Modeling of Robot Interaction in Coalition Through Smart Space and Blockchain: Precision Agriculture Scenario. In International Conference on Enterprise Information Systems (ICEIS). 481–497. – reference: Chang-Ai Sun, An Fu, Pak-Lok Poon, Xiaoyuan Xie, Huai Liu, and Tsong Yueh Chen. 2021. METRIC^++: A Metamorphic Relation Identification Technique Based on Input Plus Output Domains. IEEE Transactions on Software Engineering, 47, 9 (2021), 1764–1785. – reference: Chunqiu Steven Xia, Matteo Paltenghi, Jia Le Tian, Michael Pradel, and Lingming Zhang. 2024. Fuzz4all: Universal Fuzzing with Large Language Models. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering (ICSE). 1–13. – reference: Pierre-Luc Bacon, Jean Harb, and Doina Precup. 2017. The Option-Critic Architecture. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI), Satinder Singh and Shaul Markovitch (Eds.). AAAI Press, 1726–1734. – reference: Mayank Sharma, Pingshi Yu, and Alastair F Donaldson. 2023. Rustsmith: Random Differential Compiler Testing for Rust. In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA). 1483–1486. – reference: Chenyuan Yang, Yinlin Deng, Runyu Lu, Jiayi Yao, Jiawei Liu, Reyhaneh Jabbarvand, and Lingming Zhang. 2024. WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models. Proc. ACM Program. Lang., 8, OOPSLA2 (2024), Article 296, Oct., 27 pages. – reference: HeeSun Choi, Cindy Crump, Christian Duriez, Asher Elmquist, Gregory Hager, David Han, Frank Hearl, Jessica Hodgins, Abhinandan Jain, and Frederick Leve. 2021. On the Use of Simulation in Robotics: Opportunities, Challenges, and Suggestions for Moving Forward. Proceedings of the National Academy of Sciences, 118, 1 (2021), e1907856118. – reference: L Sopegno, P Livreri, and Kp Valavanis. 2022. Using UAVs for Future Mission on Mars. In Proceedings of the International Astronautical Congress, IAC. 2022. – reference: Tsong Yueh Chen, Fei-Ching Kuo, Huai Liu, Pak-Lok Poon, Dave Towey, T. H. Tse, and Zhi Quan Zhou. 2018. Metamorphic Testing: A Review of Challenges and Opportunities. ACM Comput. Surv., 51, 1 (2018), 4:1–4:27. – reference: Yuting Chen, Ting Su, Chengnian Sun, Zhendong Su, and Jianjun Zhao. 2016. Coverage-Directed Differential Testing of JVM Implementations. In proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI). 85–99. – reference: Musa Morena Marcusso Manhães, Sebastian A Scherer, Martin Voss, Luiz Ricardo Douat, and Thomas Rauschenbach. 2016. UUV simulator: A Gazebo-Based Package for Underwater Intervention and Multi-Robot Simulation. In Oceans 2016 Mts/IEEE Monterey. 1–8. – reference: Andrea Fioraldi, Dominik Maier, Heiko Eiß feldt, and Marc Heuse. 2020. AFL++: Combining Incremental Steps of Fuzzing Research. In 14th USENIX Workshop on Offensive Technologies (WOOT). – reference: Xiaogang Zhu, Sheng Wen, Seyit Camtepe, and Yang Xiang. 2022. Fuzzing: A Survey for Roadmap. ACM Comput. Surv., 54, 11s (2022), Article 230, Sept., 36 pages. issn:0360-0300 – reference: Mark Allan, Uland Wong, P Michael Furlong, Arno Rogg, Scott McMichael, Terry Welsh, Ian Chen, Steven Peters, Brian Gerkey, and Moraan Quigley. 2019. Planetary Rover Simulation for Lunar Exploration Missions. In 2019 IEEE Aerospace Conference. 1–19. – reference: Sergio García, Daniel Strüber, Davide Brugali, Thorsten Berger, and Patrizio Pelliccione. 2020. Robotics Software Engineering: A Perspective from the Service Robotics Domain. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE). 593–604. – reference: Mohamed Fasil Syed Ahamed, Girma Tewolde, and Jaerock Kwon. 2018. Software-in-the-Loop Modeling and Simulation Framework for Autonomous Vehicles. In 2018 IEEE International Conference on Electro/Information Technology (EIT). 0305–0310. – reference: Xiangping Chen, Xing Hu, Yuan Huang, He Jiang, Weixing Ji, Yanjie Jiang, Yanyan Jiang, Bo Liu, Hui Liu, and Xiaochen Li. 2025. Deep Learning-Based Software Engineering: Progress, Challenges, and Opportunities. Science China Information Sciences, 68, 1 (2025), 1–88. – reference: Ruijie Meng, Martin Mirchev, Marcel Böhme, and Abhik Roychoudhury. 2024. Large Language Model Guided Protocol Fuzzing. In Proceedings of the 31st Annual Network and Distributed System Security Symposium (NDSS). – reference: Olivier Michel. 2004. Cyberbotics ltd. Webots™ : Professional Mobile Robot Simulation. International Journal of Advanced Robotic Systems, 1, 1 (2004), 5. – reference: Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). – reference: Muhammad Numair Mansur, Valentin Wüstholz, and Maria Christakis. 2023. Dependency-Aware Metamorphic Testing of Datalog Engines. In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA). ACM, 236–247. – reference: Danielle Dauphinais, Michael Zylka, Harris Spahic, Farhan Shaik, Jingda Yang, Isabella Cruz, Jakob Gibson, and Ying Wang. 2023. Automated Vulnerability Testing and Detection Digital Twin Framework for 5G Systems. In 2023 IEEE 9th International Conference on Network Softwarization (NetSoft). 308–310. – reference: Michel Nass, Emil Alégroth, and Robert Feldt. 2021. Why Many Challenges with GUI Test Automation (Will) Remain. Information and Software Technology, 138 (2021), 106625. – reference: Lucas Nogueira. 2014. Comparative Analysis Between Gazebo and V-REP Robotic Simulators. Seminario Interno de Cognicao Artificial-SICA, 2014, 5 (2014), 2. – reference: Eric Rohmer, Surya PN Singh, and Marc Freese. 2013. V-REP: A Versatile and Scalable Robot Simulation Framework. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. 1321–1326. – reference: Carlos E Agüero, Nate Koenig, Ian Chen, Hugo Boyer, Steven Peters, John Hsu, Brian Gerkey, Steffi Paepcke, Jose L Rivero, and Justin Manzo. 2015. Inside the Virtual Robotics Challenge: Simulating Real-Time Robotic Disaster Response. IEEE Transactions on Automation Science and Engineering, 12, 2 (2015), 494–506. – reference: Nathan Koenig and Andrew Howard. 2004. Design and Use Paradigms for Gazebo, an Open-Source Multi-Robot Simulator. In 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 3, 2149–2154. – reference: Anastasios Andronidis and Cristian Cadar. 2022. Snapfuzz: High-Throughput Fuzzing of Network Applications. In Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA). 340–351. – reference: Afsoon Afzal, Claire Le Goues, Michael Hilton, and Christopher Steven Timperley. 2020. A Study on Challenges of Testing Robotic Systems. In 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST). 96–107. – reference: Michel Albonico, Milica Đ orđ ević, Engel Hamer, and Ivano Malavolta. 2023. Software Engineering Research on the Robot Operating System: A Systematic Mapping Study. Journal of Systems and Software, 197 (2023), 111574. – reference: Seulbae Kim and Taesoo Kim. 2022. RoboFuzz: Fuzzing Robotic Systems over Robot Operating System (ROS) for Finding Correctness Bugs. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE). 447–458. – reference: Marcela G Dos Santos, Bianca M Napoleão, Fabio Petrillo, Darine Ameyed, and Fehmi Jaafar. 2020. A Preliminary Systematic Mapping on Software Engineering for Robotic Systems: A Software Quality Perspective. In Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops. 647–654. – reference: Afsoon Afzal, Deborah S Katz, Claire Le Goues, and Christopher S Timperley. 2021. Simulation for Robotics Test Automation: Developer Perspectives. In 2021 14th IEEE Conference on Software Testing, Verification and Validation (ICST). 263–274. – reference: Megan Olsen and Mohammad Raunak. 2019. Increasing Validity of Simulation Models Through Metamorphic Testing. IEEE Transactions on Reliability, 68, 1 (2019), 91–108. – reference: Ahmed AbdelHamed, Girma Tewolde, and Jaerock Kwon. 2020. Simulation Framework for Development and Testing of Autonomous Vehicles. In 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS). 1–6. – reference: Ruyi Zhou, Wenhao Feng, Liang Ding, Huaiguang Yang, Haibo Gao, Guangjun Liu, and Zongquan Deng. 2022. MarsSim: a High-Fidelity Physical and Visual Simulation for Mars Rovers. IEEE Trans. Aerospace Electron. Systems, 59, 2 (2022), 1879–1892. – reference: Junjie Chen, Chenyao Suo, Jiajun Jiang, Peiqi Chen, and Xingjian Li. 2023. Compiler Test-Program Generation via Memoized Configuration Search. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE). 2035–2047. – reference: Junjie Chen, Haoyang Ma, and Lingming Zhang. 2020. Enhanced Compiler Bug Isolation via Memoized Search. In Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering (ASE). 78–89. – reference: Thomas Wetzlmaier, Rudolf Ramler, and Werner Putschögl. 2016. A Framework for Monkey GUI Testing. In 2016 IEEE International Conference on Software Testing, Verification and Validation (ICST). 416–423. – reference: Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan, and Chai Quek. 2022. Hierarchical Reinforcement Learning: A Comprehensive Survey. Comput. Surveys, 54, 5 (2022), 109:1–109:35. – reference: Yinlin Deng, Chunqiu Steven Xia, Chenyuan Yang, Shizhuo Dylan Zhang, Shujing Yang, and Lingming Zhang. 2024. Large Language Models are Edge-Case Generators: Crafting Unusual Programs for Fuzzing Deep Learning Libraries. In Proceedings of the 46th IEEE/ACM International Conference on Software Engineering (ICSE). ACM, 70:1–70:13. – reference: Yong-Hao Zou, Jia-Ju Bai, Jielong Zhou, Jianfeng Tan, Chenggang Qin, and Shi-Min Hu. 2021. TCP-Fuzz: Detecting Memory and Semantic Bugs in TCP Stacks with Fuzzing. In 2021 USENIX Annual Technical Conference (USENIX ATC). 489–502. – reference: Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, and Luca Antiga. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in neural information processing systems, 32 (2019). – reference: Kamak Ebadi, Lukas Bernreiter, Harel Biggie, Gavin Catt, Yun Chang, Arghya Chatterjee, Christopher E Denniston, Simon-Pierre Deschênes, Kyle Harlow, and Shehryar Khattak. 2024. Present and Future of SLAM in Extreme Environments: The DARPA SubT Challenge. IEEE Transactions on Robotics, 40 (2024), 936–959. – reference: Philippe Bardou, Jérôme Mariette, Frédéric Escudié, Christophe Djemiel, and Christophe Klopp. 2014. JVenn: an Interactive Venn Diagram Viewer. BMC bioinformatics, 15 (2014), 1–7. – reference: Gilberto Echeverria, Nicolas Lassabe, Arnaud Degroote, and Séverin Lemaignan. 2011. Modular Open Robots Simulation Engine: MORSE. In 2011 IEEE International Conference on Robotics and Automation (ICRA). 46–51. – reference: Muhammad Ali Gulzar, Yongkang Zhu, and Xiaofeng Han. 2019. Perception and Practices of Differential Testing. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). 71–80. – reference: Janis E Johnston Kenneth J Berry, Paul W Mielke Jr, Berry, and Hiripi. 2018. Measurement of Association. Springer. – reference: Kimberly A Hambuchen, Monsi C Roman, Amy Sivak, Angela Herblet, Nathan Koenig, Daniel Newmyer, and Robert Ambrose. 2017. NASA’s Space Robotics Challenge: Advancing Robotics for Future Exploration Missions. In AIAA SPACE and Astronautics Forum and Exposition. 5120. – reference: Dongwei Xiao, Zhibo Liu, and Shuai Wang. 2023. PhyFu: Fuzzing Modern Physics Simulation Engines. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE). 1579–1591. – reference: Xiaohui Wan, Tiancheng Li, Weibin Lin, Yi Cai, and Zheng Zheng. 2024. Coverage-Guided Fuzzing for Deep Reinforcement Learning Systems. Journal of Systems and Software, 210 (2024), 111963. issn:0164-1212 – reference: Patrick E McKnight and Julius Najab. 2010. Mann-Whitney U Test. The Corsini Encyclopedia of Psychology, 1–1. – ident: e_1_2_1_2_1 doi: 10.1145/3616394.3618266 – volume: 1 start-page: 5 year: 2004 ident: e_1_2_1_41_1 article-title: Cyberbotics ltd. Webots™ : Professional Mobile Robot Simulation publication-title: International Journal of Advanced Robotic Systems doi: 10.5772/5618 – volume-title: Software-in-the-Loop Modeling and Simulation Framework for Autonomous Vehicles. In 2018 IEEE International Conference on Electro/Information Technology (EIT). 0305–0310 year: 2018 ident: e_1_2_1_6_1 – volume-title: Proceedings of the 46th IEEE/ACM International Conference on Software Engineering (ICSE). ACM, 70:1–70:13 year: 2024 ident: e_1_2_1_23_1 – volume-title: 2016 IEEE International Conference on Software Testing, Verification and Validation (ICST). 416–423 year: 2016 ident: e_1_2_1_56_1 – volume-title: 14th USENIX Workshop on Offensive Technologies (WOOT). year: 2020 ident: e_1_2_1_27_1 – volume-title: Atropos: Effective Fuzzing of Web Applications for Server-Side Vulnerabilities. In USENIX Security Symposium. year: 2024 ident: e_1_2_1_29_1 – volume-title: Planetary Rover Simulation for Lunar Exploration Missions. In 2019 IEEE Aerospace Conference. 1–19 year: 2019 ident: e_1_2_1_8_1 – volume-title: JVenn: an Interactive Venn Diagram Viewer. BMC bioinformatics, 15 year: 2014 ident: e_1_2_1_12_1 – volume-title: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops. 647–654 year: 2020 ident: e_1_2_1_24_1 – volume-title: Toward Maritime Robotic Simulation in Gazebo. In OCEANS 2019 MTS/IEEE SEATTLE. 1–10 year: 2019 ident: e_1_2_1_14_1 – volume-title: V-REP: A Versatile and Scalable Robot Simulation Framework. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. 1321–1326 year: 2013 ident: e_1_2_1_50_1 – volume-title: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI), Satinder Singh and Shaul Markovitch (Eds.). AAAI Press, 1726–1734 year: 2017 ident: e_1_2_1_10_1 – volume-title: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE). 593–604 year: 2020 ident: e_1_2_1_28_1 – ident: e_1_2_1_42_1 doi: 10.1016/j.infsof.2021.106625 – volume-title: The Corsini Encyclopedia of Psychology, 1–1 ident: e_1_2_1_39_1 – volume-title: Kingma and Jimmy Ba year: 2015 ident: e_1_2_1_34_1 – volume-title: TCP-Fuzz: Detecting Memory and Semantic Bugs in TCP Stacks with Fuzzing. In 2021 USENIX Annual Technical Conference (USENIX ATC). 489–502 year: 2021 ident: e_1_2_1_63_1 – volume: 2014 start-page: 2 year: 2014 ident: e_1_2_1_44_1 article-title: Comparative Analysis Between Gazebo and V-REP Robotic Simulators publication-title: Seminario Interno de Cognicao Artificial-SICA – volume: 47 start-page: 1764 year: 2021 ident: e_1_2_1_54_1 article-title: METRIC^++: A Metamorphic Relation Identification Technique Based on Input Plus Output Domains publication-title: IEEE Transactions on Software Engineering – volume-title: Robot Operating System 2: Design, architecture, and uses in the wild. Science Robotics, 7, 66 year: 2022 ident: e_1_2_1_36_1 – volume-title: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE). 2035–2047 year: 2023 ident: e_1_2_1_16_1 – volume-title: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 3, 2149–2154 year: 2004 ident: e_1_2_1_35_1 – volume-title: Proceedings of the 18th International Conference on Software Technologies (ICSOFT). SciTePress, 151–162 year: 2023 ident: e_1_2_1_46_1 – volume-title: Hierarchical Reinforcement Learning: A Comprehensive Survey. Comput. Surveys, 54, 5 year: 2022 ident: e_1_2_1_48_1 – ident: e_1_2_1_33_1 doi: 10.1145/3540250.3549164 – ident: e_1_2_1_7_1 doi: 10.1016/j.jss.2022.111574 – volume: 118 start-page: e1907856118 year: 2021 ident: e_1_2_1_20_1 article-title: On the Use of Simulation in Robotics: Opportunities, Challenges, and Suggestions for Moving Forward publication-title: Proceedings of the National Academy of Sciences doi: 10.1073/pnas.1907856118 – volume-title: NASA’s Space Robotics Challenge: Advancing Robotics for Future Exploration Missions. In AIAA SPACE and Astronautics Forum and Exposition. 5120 year: 2017 ident: e_1_2_1_31_1 – volume-title: Perception and Practices of Differential Testing. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). 71–80 year: 2019 ident: e_1_2_1_30_1 – ident: e_1_2_1_22_1 doi: 10.1109/NetSoft57336.2023.10175496 – volume-title: Modeling of Robot Interaction in Coalition Through Smart Space and Blockchain: Precision Agriculture Scenario. In International Conference on Enterprise Information Systems (ICEIS). 481–497 year: 2020 ident: e_1_2_1_52_1 – volume-title: Modular Open Robots Simulation Engine: MORSE. In 2011 IEEE International Conference on Robotics and Automation (ICRA). 46–51 year: 2011 ident: e_1_2_1_26_1 – ident: e_1_2_1_40_1 doi: 10.14722/ndss.2024.24556 – volume-title: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). 763–778 year: 2024 ident: e_1_2_1_11_1 – ident: e_1_2_1_18_1 doi: 10.1007/s11432-023-4127-5 – volume-title: Proceedings of the International Astronautical Congress, IAC. year: 2022 ident: e_1_2_1_53_1 – ident: e_1_2_1_32_1 doi: 10.1109/TCE.2023.3269528 – volume-title: Luiz Ricardo Douat, and Thomas Rauschenbach year: 2016 ident: e_1_2_1_37_1 – ident: e_1_2_1_49_1 doi: 10.1109/ICST46399.2020.00062 – volume-title: Simulation for Robotics Test Automation: Developer Perspectives. In 2021 14th IEEE Conference on Software Testing, Verification and Validation (ICST). 263–274 year: 2021 ident: e_1_2_1_3_1 – volume-title: PyTorch: An Imperative Style year: 2019 ident: e_1_2_1_47_1 – volume-title: Fuzzing: A Survey for Roadmap. ACM Comput. Surv., 54, 11s year: 2022 ident: e_1_2_1_62_1 – volume: 6 start-page: 477 year: 2023 ident: e_1_2_1_21_1 article-title: Into the Robotic Depths: Analysis and Insights from the DARPA Subterranean Challenge publication-title: Annual Review of Control, Robotics, and Autonomous Systems doi: 10.1146/annurev-control-062722-100728 – volume: 51 year: 2018 ident: e_1_2_1_17_1 article-title: Metamorphic Testing publication-title: A Review of Challenges and Opportunities. ACM Comput. Surv. – ident: e_1_2_1_43_1 doi: 10.1007/s10664-022-10233-3 – volume-title: PhyFu: Fuzzing Modern Physics Simulation Engines. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE). 1579–1591 year: 2023 ident: e_1_2_1_58_1 – volume: 59 start-page: 1879 year: 2022 ident: e_1_2_1_61_1 article-title: MarsSim: a High-Fidelity Physical and Visual Simulation for Mars Rovers publication-title: IEEE Trans. Aerospace Electron. Systems – ident: e_1_2_1_25_1 doi: 10.1109/TRO.2023.3323938 – volume-title: Coverage-Directed Differential Testing of JVM Implementations. In proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI). 85–99 year: 2016 ident: e_1_2_1_19_1 – volume-title: Paul W Mielke Jr, Berry, and Hiripi year: 2018 ident: e_1_2_1_13_1 – volume-title: Proceedings of the IEEE/ACM 46th International Conference on Software Engineering (ICSE). 1–13 year: 2024 ident: e_1_2_1_57_1 – ident: e_1_2_1_55_1 doi: 10.1016/j.jss.2024.111963 – ident: e_1_2_1_38_1 doi: 10.1145/3597926.3598052 – ident: e_1_2_1_45_1 doi: 10.1109/TR.2018.2850315 – ident: e_1_2_1_5_1 doi: 10.1109/TASE.2014.2368997 – ident: e_1_2_1_15_1 doi: 10.1145/3324884.3416570 – volume-title: ROZZ: Property-Based Fuzzing for Robotic Programs in ROS. In 2022 International Conference on Robotics and Automation (ICRA). 6786–6792 year: 2022 ident: e_1_2_1_59_1 – volume-title: Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA). 1483–1486 year: 2023 ident: e_1_2_1_51_1 – volume-title: Electronics and Mechatronics Conference (IEMTRONICS). 1–6. year: 2020 ident: e_1_2_1_1_1 – volume-title: Proc. ACM Program. Lang., 8, OOPSLA2 year: 2024 ident: e_1_2_1_60_1 – ident: e_1_2_1_4_1 doi: 10.1109/ICST46399.2020.00020 – ident: e_1_2_1_9_1 doi: 10.1145/3533767.3534376
SSID	ssj0002991170
Score	2.2951112
Snippet	Gazebo, being the most widely utilized simulator in robotics, plays a pivotal role in developing and testing robotic systems. Given its impact on the safety...
SourceID	crossref acm
SourceType	Index Database Publisher
StartPage	1467
SubjectTerms	Software and its engineering Software testing and debugging
SubjectTermsDisplay	Software and its engineering -- Software testing and debugging
Title	Reinforcement Learning-Based Fuzz Testing for the Gazebo Robotic Simulator
URI	https://dl.acm.org/doi/10.1145/3728942
Volume	2
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2994-970X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002991170 issn: 2994-970X databaseCode: M~E dateStart: 20240101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1BT9swFLYK48BlY8A0toF84IaipY4Tx8cOgQYSCNYcul1Q7DgQaSSoLVBx4B_sP_Ne7KQpQmI77BJVrh1Ffl_ee3Y-f4-Q3TjPlNA89DQLM49L04dXKmdeGvh5pmXKlLbFJsTpaTwaybNe709zFubutyjLeDaTN__V1NAGxsajs_9g7vam0AC_wehwBbPD9a8M_8PUYqi63vdr9FMvvW8QrrK9w9uHh70ElTUcgRLzTiT8qApJ1hXKtw6La6zpVY27ietZG-gmDa1gsH-Cnxom4MjvkT9m5tKG7Wcc69R-XYHvKVruT00g-FlM02qxaVSkWL-rhet53QogLmeF6-v2J1iIPCrW2bJkKD4shT-yEeeFNueHWQduR8NhMuj4VfTnnRgNa7j4Zf_PUSojELCMtKJdiwrbzyJfy0e0p7PDCzdwibxhIpTIEDx5nG_ZwVNjnR4sV9g8vz2GjWO_urGY5ejrTpbTSVeSNfLWrTPowOLjPemZcp28a2p4UOfSN8jxAlzoIlwowoU6uFDoRcHu1MKFOrjQFi6bJDk8SPa_e66-hpdCmudFKs1FkErBNKQsfRVhpQQjtJ_7OVcMk0Hu5wJpRVoFsZGByXjGYhNDYIoMDz6Q5bIqzUdCM8VlptKQB0xzniuFYldRYPpKRRoWCFtkHSbl4sYKqDTTvEVoM0ntX88s8en1Lp_J6hx2X8jydHxrtsmKvpsWk_FObcEn_wlsFg
linkProvider	ISSN International Centre
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Reinforcement+Learning-Based+Fuzz+Testing+for+the+Gazebo+Robotic+Simulator&rft.jtitle=Proceedings+of+the+ACM+on+software+engineering&rft.au=Ren%2C+Zhilei&rft.au=Li%2C+Yitao&rft.au=Li%2C+Xiaochen&rft.au=Qi%2C+Guanxiao&rft.date=2025-06-22&rft.issn=2994-970X&rft.eissn=2994-970X&rft.volume=2&rft.issue=ISSTA&rft.spage=1467&rft.epage=1488&rft_id=info:doi/10.1145%2F3728942&rft.externalDBID=n%2Fa&rft.externalDocID=10_1145_3728942
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2994-970X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2994-970X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2994-970X&client=summon