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ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutation

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Xiangyu ZhangAuthors Info & Claims

ASE '24: Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering

Pages 1620 - 1632

https://doi.org/10.1145/3691620.3695530

Published: 27 October 2024 Publication History

Abstract

As Autonomous driving systems (ADS) have transformed our daily life, safety of ADS is of growing significance. While various testing approaches have emerged to enhance the ADS reliability, a crucial gap remains in understanding the accidents causes. Such post-accident analysis is paramount and beneficial for enhancing ADS safety and reliability. Existing cyber-physical system (CPS) root cause analysis techniques are mainly designed for drones and cannot handle the unique challenges introduced by more complex physical environments and deep learning models deployed in ADS. In this paper, we address the gap by offering a formal definition of ADS root cause analysis problem and introducing Rocas, a novel ADS root cause analysis framework featuring cyber-physical co-mutation. Our technique uniquely leverages both physical and cyber mutation that can precisely identify the accident-trigger entity and pinpoint the misconfiguration of the target ADS responsible for an accident. We further design a differential analysis to identify the responsible module to reduce search space for the misconfiguration. We study 12 categories of ADS accidents and demonstrate the effectiveness and efficiency of Rocas in narrowing down search space and pinpointing the misconfiguration. We also show detailed case studies on how the identified misconfiguration helps understand rationale behind accidents.

References

[1]

[n. d.]. American Fuzzy Lop. https://lcamtuf.coredump.cx/afl/

[2]

[n. d.]. Baidu Apollo. https://github.com/ApolloAuto/apollo

[3]

[n. d.]. Baidu Apollo CyberRT. https://cyber-rt.readthedocs.io/en/latest/

[4]

[n. d.]. CARLA simulator. https://carla.org/

[5]

[n. d.]. LGSVL simulator. https://www.svlsimulator.com/

[6]

[n. d.]. ROCAS Artifact Repo. https://github.com/GiantSeaweed/ROCAS

[7]

[n. d.]. TESLA KEEPS "SLAMMING ON THE BRAKES" WHEN IT SEES STOP SIGN ON BILLBOARD. https://futurism.com/the-byte/tesla-slamming-brakes-sees-stop-sign-billboard

[8]

[n. d.]. Waymo. 2020. Off road, but not offline: How simulation helps advance our Waymo Driver. https://blog.waymo.com/2020/04/off-road-but-not-offline-simulation27.html

[9]

2016. BBC News. 2016. Google Self-driving Car Hits a Bus. https://www.bbc.com/news/technology-35692845

[10]

2016. BBC News. 2016. Uber in Fatal Crash Had Safety Flaws Say US Investigators. https://www.bbc.com/news/business-50312340

[11]

2019. BBC News. 2019. Tesla Model 3: Autopilot Engaged during Fatal Crash. https://www.bbc.com/news/technology-48308852

[12]

2020. Associated Press News. 2020. 3 Crashes, 3 Deaths Raise Questions About Tesla's Autopilot. https://apnews.com/article/europe-us-news-ap-top-news-in-state-wire-mi-state-wire-ca5e62255bb87bf1b151f9bf075aaadf

[13]

2021. CARLA AD Challenge. https://leaderboard.carla.org/.

[14]

2023. WIRED. 2023. Dashcam Footage Shows Driverless Cars Clogging San Francisco. https://www.wired.com/story/dashcam-footage-shows-driverless-cars-cruise-waymo-clogging-san-francisco/

[15]

Marcel Böhme, Van-Thuan Pham, Manh-Dung Nguyen, and Abhik Roychoudhury. 2017. Directed Greybox Fuzzing. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (Dallas, Texas, USA) (CCS '17). Association for Computing Machinery, New York, NY, USA, 2329--2344.

Digital Library

[16]

Erik Bosman, Asia Slowinska, and Herbert Bos. 2011. Minemu: The world's fastest taint tracker. In Recent Advances in Intrusion Detection: 14th International Symposium, RAID 2011, Menlo Park, CA, USA, September 20--21, 2011. Proceedings 14. Springer, 1--20.

Digital Library

[17]

Yuriy Brun and Michael D Ernst. 2004. Finding latent code errors via machine learning over program executions. In Proceedings. 26th International Conference on Software Engineering. IEEE, 480--490.

Digital Library

[18]

Giuseppe Cascavilla, Johann Slabber, Fabio Palomba, Dario Di Nucci, Damian A. Tamburri, and Willem-Jan van den Heuvel. 2020. Counterterrorism for Cyber-Physical Spaces: A Computer Vision Approach. In Proceedings of the International Conference on Advanced Visual Interfaces (Salerno, Italy) (AVI '20). Association for Computing Machinery, New York, NY, USA, Article 52, 5 pages.

Digital Library

[19]

Ramesh Chandra, Taesoo Kim, Meelap Shah, Neha Narula, and Nickolai Zeldovich. 2011. Intrusion recovery for database-backed web applications. In Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles. 101--114.

Digital Library

[20]

Dian Chen and Philipp Krähenbühl. 2022. Learning from all vehicles. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 17222--17231.

[21]

Zhiyuan Cheng, Hongjun Choi, Shiwei Feng, James Chenhao Liang, Guanhong Tao, Dongfang Liu, Michael Zuzak, and Xiangyu Zhang. 2024. Fusion Is Not Enough: Single Modal Attacks on Fusion Models for 3D Object Detection. In The Twelfth International Conference on Learning Representations.

[22]

Zhiyuan Cheng, Zhaoyi Liu, Tengda Guo, Shiwei Feng, Dongfang Liu, Mingjie Tang, and Xiangyu Zhang. 2024. BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks. In Forty-first International Conference on Machine Learning.

[23]

Hongjun Choi, Zhiyuan Cheng, and Xiangyu Zhang. 2022. RVPLAYER: Robotic Vehicle Forensics by Replay with What-if Reasoning. In 29th Annual Network and Distributed System Security Symposium, NDSS 2022, San Diego, California, USA, April 24--28, 2022. The Internet Society. https://www.ndss-symposium.org/ndss-paper/auto-draft-215/

[24]

Hongjun Choi, Sayali Kate, Yousra Aafer, Xiangyu Zhang, and Dongyan Xu. 2020. Cyber-Physical Inconsistency Vulnerability Identification for Safety Checks in Robotic Vehicles.

Digital Library

[25]

Devon R Clark, Christopher Meffert, Ibrahim Baggili, and Frank Breitinger. 2017. DROP (DRone Open source Parser) your drone: Forensic analysis of the DJI Phantom III. Digital Investigation 22 (2017), S3--S14.

Digital Library

[26]

James Clause, Wanchun Li, and Alessandro Orso. 2007. Dytan: a generic dynamic taint analysis framework. In Proceedings of the 2007 international symposium on Software testing and analysis. 196--206.

Digital Library

[27]

Raphael Pereira de Oliveira, Paulo Anselmo da Mota Silveira Neto, Qi Hong Chen, Eduardo Santana de Almeida, and Iftekhar Ahmed. 2022. Different, Really! A comparison of Highly-Configurable Systems and Single Systems. Information and Software Technology 152 (2022), 107035.

[28]

Kalyanmoy Deb and Deb Kalyanmoy. 2001. Multi-Objective Optimization Using Evolutionary Algorithms. USA.

[29]

Hina Anwar Dietmar Pfahl Fauzia Khan, Laima Dalbina. 2023. How Can Simulation-based Safety Testing Help Understand the Real-World Safety of Autonomous Driving Systems? WORKS in PROGRESS in EMBEDDED COMPUTING (WiPiEC) (2023).

[30]

Shiwei Feng, Yapeng Ye, Qingkai Shi, Zhiyuan Cheng, Xiangzhe Xu, Siyuan Cheng, Hongjun Choi, and Xiangyu Zhang. 2024. ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutation. In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering (ASE 2024).

Digital Library

[31]

Joshua Garcia, Yang Feng, Junjie Shen, Sumaya Almanee, Yuan Xia, Chen, and Qi Alfred. 2020. A Comprehensive Study of Autonomous Vehicle Bugs. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (Seoul, South Korea) (ICSE '20). Association for Computing Machinery, New York, NY, USA, 385--396.

Digital Library

[32]

Dennis Geels, Gautam Altekar, Petros Maniatis, Timothy Roscoe, and Ion Stoica. 2007. Friday: Global Comprehension for Distributed Replay. In NSDI.

[33]

Ashvin Goel, Kenneth Po, Kamran Farhadi, Zheng Li, and Eyal De Lara. 2005. The taser intrusion recovery system. In Proceedings of the twentieth ACM symposium on Operating systems principles. 163--176.

Digital Library

[34]

Zhenyu Guo, Xi Wang, Jian Tang, Xuezheng Liu, Zhilei Xu, Ming Wu, M Frans Kaashoek, and Zheng Zhang. 2008. R2: An Application-Level Kernel for Record and Replay. In OSDI, Vol. 8. 193--208.

[35]

Huong Ha and Hongyu Zhang. 2019. DeepPerf: Performance Prediction for Configurable Software with Deep Sparse Neural Network. In Proceedings of the 41st International Conference on Software Engineering (Montreal, Quebec, Canada) (ICSE '19). IEEE Press, 1095--1106.

Digital Library

[36]

Gor Hakobyan and Bin Yang. 2019. High-performance automotive radar: A review of signal processing algorithms and modulation schemes. IEEE Signal Processing Magazine 36, 5 (2019), 32--44.

[37]

Ruidong Han, Chao Yang, Siqi Ma, JiangFeng Ma, Cong Sun, Juanru Li, and Elisa Bertino. 2022. Control parameters considered harmful: Detecting range specification bugs in drone configuration modules via learning-guided search. In Proceedings of the 44th International Conference on Software Engineering. 462--473.

Digital Library

[38]

Xue Han and Tingting Yu. 2016. An Empirical Study on Performance Bugs for Highly Configurable Software Systems. In Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (Ciudad Real, Spain) (ESEM '16). Association for Computing Machinery, New York, NY, USA, Article 23, 10 pages.

Digital Library

[39]

Xue Han, Tingting Yu, and David Lo. 2018. PerfLearner: Learning from Bug Reports to Understand and Generate Performance Test Frames. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (Montpellier, France) (ASE '18). Association for Computing Machinery, New York, NY, USA, 17--28.

Digital Library

[40]

Mary Jean et al. Harrold. 2000. An empirical investigation of the relationship between spectra differences and regression faults. Software Testing, Verification and Reliability 10, 3 (2000), 171--194.

[41]

Haochen He, Zhouyang Jia, Shanshan Li, Erci Xu, Tingting Yu, Yue Yu, Ji Wang, and Xiangke Liao. 2020. CP-Detector: Using Configuration-related Performance Properties to Expose Performance Bugs. In 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE). 623--634.

Digital Library

[42]

Md Abir Hossen, Sonam Kharade, Bradley Schmerl, Javier Cámara, Jason M O'Kane, Ellen C Czaplinski, Katherine A Dzurilla, David Garlan, and Pooyan Jamshidi. 2023. CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robots. IEEE Robotics and Automation Letters (2023).

[43]

Daniel Howard and Danielle Dai. 2014. Public perceptions of self-driving cars: The case of Berkeley, California. In Transportation research board 93rd annual meeting. The National Academies of Sciences, Engineering, and Medicine Washington, DC.

[44]

Yuqi Huai, Yuntianyi Chen, Sumaya Almanee, Tuan Ngo, Xiang Liao, Ziwen Wan, Qi Alfred Chen, and Joshua Garcia. 2023. Doppelganger Test Generation for Revealing Bugs in Autonomous Driving Software. In ACM/IEEE 45th International Conference on Software Engineering. Melbourne, Australia.

[45]

Dmytro Humeniuk, Foutse Khomh, and Giuliano Antoniol. 2022. A search-based framework for automatic generation of testing environments for cyber-physical systems. Information and Software Technology 149 (2022), 106936.

Digital Library

[46]

Upasita Jain, Marcus Rogers, and Eric T Matson. 2017. Drone forensic framework: Sensor and data identification and verification. In 2017 IEEE Sensors Applications Symposium (SAS). IEEE, 1--6.

Digital Library

[47]

Chijung Jung, Ali Ahad, Yuseok Jeon, and Yonghwi Kwon. 2022. SwarmFlawFinder: Discovering and Exploiting Logic Flaws of Swarm Algorithms. In 43rd IEEE Symposium on Security and Privacy, SP 2022, San Francisco, CA, USA, 23--26 May 2021. IEEE, 1447--1464.

[48]

Chijung Jung, Ali Ahad, Jinho Jung, Sebastian Elbaum, and Yonghwi Kwon. 2021. Swarmbug: Debugging Configuration Bugs in Swarm Robotics. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021). Association for Computing Machinery, 868--880.

Digital Library

[49]

Min Gyung et al. Kang. [n. d.]. Dta++: dynamic taint analysis with targeted control-flow propagation. In NDSS 2011.

[50]

Ranjita Pai Kasturi, Yiting Sun, Ruian Duan, Omar Alrawi, Ehsan Asdar, Victor Zhu, Yonghwi Kwon, and Brendan Saltaformaggio. 2020. TARDIS: Rolling back the clock on CMS-targeting cyber attacks. In 2020 IEEE Symposium on Security and Privacy (SP). IEEE, 1156--1171.

[51]

R. Killick, P. Fearnhead, and I. A. Eckley. 2012. Optimal Detection of Changepoints With a Linear Computational Cost. J. Amer. Statist. Assoc. 107, 500 (oct 2012), 1590--1598.

[52]

Hyungsub Kim, Muslum Ozgur Ozmen, Antonio Bianchi, Z Berkay Celik, and Dongyan Xu. 2021. PGFUZZ: Policy-Guided Fuzzing for Robotic Vehicles. In Proceedings of the Network and Distributed System Security Symposium (NDSS).

[53]

Seulbae Kim and Taesoo Kim. [n. d.]. 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 2022).

Digital Library

[54]

Seulbae Kim, Major Liu, Junghwan "John" Rhee, Yuseok Jeon, Yonghwi Kwon, and Chung Hwan Kim. 2022. DriveFuzz: Discovering Autonomous Driving Bugs through Driving Quality-Guided Fuzzing. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (Los Angeles, CA, USA) (CCS 2022). ACM, 1753--1767.

Digital Library

[55]

Taegyu Kim, Chung Hwan Kim, Altay Ozen, Fan Fei, Zhan Tu, Xiangyu Zhang, Xinyan Deng, Dave (Jing) Tian, and Dongyan Xu. [n. d.]. From Control Model to Program: Investigating Robotic Aerial Vehicle Accidents with MAYDAY. In 29th USENIX Security Symposium (SEC'20).

[56]

Taegyu Kim, Chung Hwan Kim, Junghwan Rhee, Fan Fei, Zhan Tu, Gregory Walkup, Xiangyu Zhang, Xinyan Deng, and Dongyan Xu. 2019. RVFUZZER: Finding Input Validation Bugs in Robotic Vehicles through Control-Guided Testing. In Proceedings of the 28th USENIX Conference on Security Symposium (SEC'19).

[57]

Taesoo Kim, Xi Wang, Nickolai Zeldovich, M Frans Kaashoek, et al. 2010. Intrusion Recovery Using Selective Re-execution. In OSDI. 89--104.

[58]

Samuel T King and Peter M Chen. 2003. Backtracking intrusions. In Proceedings of the nineteenth ACM symposium on Operating systems principles. 223--236.

Digital Library

[59]

Rahul Krishna, Md Shahriar Iqbal, Mohammad Ali Javidian, Baishakhi Ray, and Pooyan Jamshidi. 2020. Cadet: Debugging and fixing misconfigurations using counterfactual reasoning. arXiv preprint arXiv:2010.06061 (2020).

[60]

Srinivas Krishnan, Kevin Z Snow, and Fabian Monrose. 2010. Trail of bytes: efficient support for forensic analysis. In Proceedings of the 17th ACM conference on Computer and communications security. 50--60.

Digital Library

[61]

Bijun Lee, Yang Wei, and I Yuan Guo. 2017. Automatic parking of self-driving car based on lidar. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci (2017).

[62]

Henry C Lee and Elaine M Pagliaro. 2013. Forensic evidence and crime scene investigation. Journal of Forensic Investigation 1, 2 (2013), 1--5.

[63]

Jaekwon Lee, Enrico Viganò, Oscar Cornejo, Fabrizio Pastore, and Lionel Briand. 2023. Fuzzing for CPS Mutation Testing. arXiv:2308.07949 [cs.SE]

[64]

Kyu Hyung Lee, Xiangyu Zhang, and Dongyan Xu. 2013. High Accuracy Attack Provenance via Binary-based Execution Partition. In NDSS, Vol. 16.

[65]

Wen Li, Jinyang Ruan, Guangbei Yi, Long Cheng, Xiapu Luo, and Haipeng Cai. 2023. PolyFuzz: Holistic Greybox Fuzzing of Multi-Language Systems. In 32nd USENIX Security Symposium (USENIX Security 23). USENIX Association, Anaheim, CA, 1379--1396. https://www.usenix.org/conference/usenixsecurity23/presentation/li-wen (artifact evaluated; badges: Available).

[66]

Prianka Mandal, Sunil Manandhar, Kaushal Kafle, Kevin Moran, Denys Poshyvanyk, and Adwait Nadkarni. 2023. Helion: Enabling Natural Testing of Smart Homes. arXiv preprint arXiv:2308.06695 (2023).

[67]

Rui Mei, Hanbing Yan, Qinqin Wang, Zhihui Han, and Zhuohang Lyu. 2022. TDLens: toward an empirical evaluation of provenance graph-based approach to cyber threat detection. China Communications 19, 10 (2022), 102--115.

[68]

Claudio Menghi, Enrico Viganò, Domenico Bianculli, and Lionel C Briand. 2021. Trace-checking CPS properties: Bridging the cyber-physical gap. In IEEE/ACM 43rd International Conference on Software Engineering (ICSE).

Digital Library

[69]

Satish Narayanasamy, Gilles Pokam, and Brad Calder. 2006. Bugnet: Recording application-level execution for deterministic replay debugging. IEEE Micro 26, 1 (2006), 100--109.

Digital Library

[70]

Peter Ohmann and Ben Liblit. 2017. Lightweight control-flow instrumentation and postmortem analysis in support of debugging. Automated Software Engineering 24 (2017), 865--904.

Digital Library

[71]

Qi Pang, Yuanyuan Yuan, and Shuai Wang. 2022. MDPFuzz: Testing Models Solving Markov Decision Processes. In Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2022). 13 pages.

Digital Library

[72]

Ruoyu Song, Muslum Ozgur Ozmen, Hyungsub Kim, Raymond Muller, Z. Berkay Celik, and Antonio Bianchi. 2023. Discovering Adversarial Driving Maneuvers against Autonomous Vehicles. In USENIX Security Symposium.

[73]

Sudarshan M Srinivasan, Srikanth Kandula, Christopher R Andrews, Yuanyuan Zhou, et al. 2004. Flashback: A lightweight extension for rollback and deterministic replay for software debugging. In USENIX Annual Technical Conference, General Track. Boston, MA, USA, 29--44.

[74]

Huijia Sun, Christopher M Poskitt, Yang Sun, Jun Sun, and Yuqi Chen. 2024. ACAV: A Framework for Automatic Causality Analysis in Autonomous Vehicle Accident Recordings. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering. 1--13.

Digital Library

[75]

Yun Tang, Yuan Zhou, Yang Liu, Jun Sun, and Gang Wang. 2021. Collision Avoidance Testing for Autonomous Driving Systems on Complete Maps. In 2021 IEEE Intelligent Vehicles Symposium (IV). 179--185.

Digital Library

[76]

Yun Tang, Yuan Zhou, Fenghua Wu, Yang Liu, Jun Sun, Wuling Huang, and Gang Wang. 2021. Route coverage testing for autonomous vehicles via map modeling. In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 11450--11456.

Digital Library

[77]

Yun Tang, Yuan Zhou, Tianwei Zhang, Fenghua Wu, Yang Liu, and Gang Wang. 2021. Systematic Testing of Autonomous Driving Systems Using Map Topology-Based Scenario Classification. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE). 1342--1346.

Digital Library

[78]

Pablo Valle, Aitor Arrieta, and Maite Arratibel. 2023. Automated Misconfiguration Repair of Configurable Cyber-Physical Systems with Search: an Industrial Case Study on Elevator Dispatching Algorithms. In 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). IEEE.

Digital Library

[79]

Miguel Velez, Pooyan Jamshidi, Norbert Siegmund, Sven Apel, and Christian Kästner. 2022. On Debugging the Performance of Configurable Software Systems: Developer Needs and Tailored Tool Support. In Proceedings of the 44th International Conference on Software Engineering (ICSE). ACM Press, New York, NY, 1571--1583.

Digital Library

[80]

Gregory Walkup, Sriharsha Etigowni, Dongyan Xu, Vincent Urias, and Han W Lin. 2020. Forensic Investigation of Industrial Control Systems Using Deterministic Replay. In 2020 IEEE Conference on Communications and Network Security (CNS). IEEE, 1--9.

[81]

Ziwen Wan, Junjie Shen, Jalen Chuang, Xin Xia, Joshua Garcia, Jiaqi Ma, and Qi Alfred Chen. 2022. Too Afraid to Drive: Systematic Discovery of Semantic DoS Vulnerability in Autonomous Driving Planning under Physical-World Attacks. In Network and Distributed System Security (NDSS) Symposium, 2022.

[82]

Tong et al. Wang. 2024. Dance of the ADS: Orchestrating Failures through Historically-Informed Scenario Fuzzing. (2024).

[83]

Mark Weiser. 1984. Program slicing. IEEE Transactions on software engineering 4 (1984), 352--357.

Digital Library

[84]

W Eric Wong, Ruizhi Gao, Yihao Li, Rui Abreu, and Franz Wotawa. 2016. A survey on software fault localization. IEEE Transactions on Software Engineering 42, 8 (2016), 707--740.

Digital Library

[85]

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). IEEE, 6786--6792.

[86]

Xiaofei Xie, Hongxu Chen, Yi Li, Lei Ma, Yang Liu, and Jianjun Zhao. 2019. Coverage-Guided Fuzzing for FeedForward Neural Networks. In Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). 1162--1165.

Digital Library

[87]

Ibrar Yaqoob, Latif U Khan, SM Ahsan Kazmi, Muhammad Imran, Nadra Guizani, and Choong Seon Hong. 2019. Autonomous driving cars in smart cities: Recent advances, requirements, and challenges. IEEE Network (2019).

[88]

Ding Yuan, Haohui Mai, Weiwei Xiong, Lin Tan, Yuanyuan Zhou, and Shankar Pasupathy. 2010. Sherlog: error diagnosis by connecting clues from run-time logs. In Proceedings of the fifteenth International Conference on Architectural support for programming languages and operating systems. 143--154.

Digital Library

[89]

Fiorella Zampetti, Ritu Kapur, Massimiliano Di Penta, and Sebastiano Panichella. 2022. An empirical characterization of software bugs in open-source Cyber-Physical Systems. J. Syst. Softw. 192 (2022), 111425.

Digital Library

[90]

Andreas Zeller. 2002. Isolating cause-effect chains from computer programs. In Proceedings of the 10th ACM SIGSOFT Symposium on Foundations of Software Engineering (SIGSOFT '02/FSE-10). Association for Computing Machinery.

Digital Library

[91]

Jun Zeng, Chuqi Zhang, and Zhenkai Liang. 2022. PalanTír: Optimizing Attack Provenance with Hardware-enhanced System Observability. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security. 3135--3149.

Digital Library

[92]

Alice X Zheng, Michael I Jordan, Ben Liblit, Mayur Naik, and Alex Aiken. 2006. Statistical debugging: simultaneous identification of multiple bugs. In Proceedings of the 23rd international conference on Machine learning. 1105--1112.

Digital Library

[93]

Ziyuan Zhong, Zhisheng Hu, Shengjian Guo, Xinyang Zhang, Zhenyu Zhong, and Baishakhi Ray. 2022. Detecting multi-sensor fusion errors in advanced driver-assistance systems. In Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis. 493--505.

Digital Library

[94]

Ziyuan Zhong, Yun Tang, Yuan Zhou, Vania de Oliveira Neves, Yang Liu, and Baishakhi Ray. 2021. A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation. arXiv:2112.00964 [cs.SE]

Cited By

Feng SYe YShi QCheng ZXu XCheng SChoi HZhang XFilkov VRay BZhou M(2024)ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutationProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695530(1620-1632)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695530

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cover image ACM Conferences

ASE '24: Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering

October 2024

2587 pages

ISBN:9798400712487

DOI:10.1145/3691620

General Chair:
Vladimir Filkov,
Program Co-chairs:
Baishakhi Ray
Columbia University, USA; AWS AI Lab
,
Minghui Zhou
Peking University, China

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Feng SYe YShi QCheng ZXu XCheng SChoi HZhang XFilkov VRay BZhou M(2024)ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutationProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695530(1620-1632)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695530

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