{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T11:59:36Z","timestamp":1790423976685,"version":"4.1.0"},"reference-count":99,"publisher":"Association for Computing Machinery (ACM)","issue":"OOPSLA1","license":[{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Program. Lang."],"published-print":{"date-parts":[[2026,4,10]]},"abstract":"<jats:p>\n                    We present\n                    <jats:sc>Prunario<\/jats:sc>\n                    , a novel technique for effectively testing autonomous driving systems (ADS). Ensuring the safety of ADS is critical, as their failures can lead to severe casualties. While ADS testing methods have advanced in recent years, they remain unsatisfactory in generating diverse test scenarios that induce distinct driving behaviors\u2013a key requirement for thoroughly evaluating ADS across different situations. To address this,\n                    <jats:sc>Prunario<\/jats:sc>\n                    employs a novel simulation prediction technique to estimate ADS runtime behavior and prune redundant test scenarios that yield similar driving records. Experimental results demonstrate\n                    <jats:sc>Prunario<\/jats:sc>\n                    \u2019s effectiveness: it uncovered 23 previously undetected bugs in an industrial-strength ADS and outperformed three state-of-the-art testing techniques.\n                  <\/jats:p>","DOI":"10.1145\/3798262","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T14:33:43Z","timestamp":1775831623000},"page":"1707-1736","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Prunario: Testing Autonomous Driving Systems by Pruning Likely Redundant Scenarios"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6663-6489","authenticated-orcid":false,"given":"Minsu","family":"Kim","sequence":"first","affiliation":[{"name":"Korea University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7005-1928","authenticated-orcid":false,"given":"Sunbeom","family":"So","sequence":"additional","affiliation":[{"name":"Korea University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1900-7654","authenticated-orcid":false,"given":"Hakjoo","family":"Oh","sequence":"additional","affiliation":[{"name":"Korea University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"[n.d.]. A demo video for Autoware\u2019s autonomous valet parking. https:\/\/www.youtube.com\/watch?v=MC7n8vwiLcg. Accessed: October 2025."},{"key":"e_1_3_1_3_2","unstructured":"[n.d.]. A demo video for Autoware\u2019s cargo delivery. https:\/\/www.youtube.com\/watch?v=JQArZ-z9Zjc. Accessed: October 2025."},{"key":"e_1_3_1_4_2","unstructured":"[n. d.]. A GitHub repository for Autoware Core. https:\/\/github.com\/autowarefoundation\/autoware_core. Accessed: October 2025."},{"key":"e_1_3_1_5_2","unstructured":"[n.d.]. A GitHub repository for Autoware Universe. https:\/\/github.com\/autowarefoundation\/autoware_universe. Accessed: October 2025."},{"key":"e_1_3_1_6_2","unstructured":"[n. d.]. A GitHub repository of Apollo. https:\/\/github.com\/ApolloAuto\/apollo. Accessed: October 2025."},{"key":"e_1_3_1_7_2","unstructured":"[n. d.]. A URL for the Behavior Agent. https:\/\/carla.readthedocs.io\/en\/0.9.15\/adv_agents\/. Accessed: October 2025."},{"key":"e_1_3_1_8_2","unstructured":"[n. d.]. An issue report for the error in CARLA simulator which leads to false positives. https:\/\/github.com\/carla-simulator\/carla\/issues\/5693. Accessed: October 2025."},{"key":"e_1_3_1_9_2","unstructured":"[n. d.]. An URL for Autopilot. https:\/\/www.tesla.com\/autopilot. Accessed: October 2025."},{"key":"e_1_3_1_10_2","unstructured":"[n. d.]. An URL for bridge implementation for Autoware 7877192 4a3de49 and 75549a6. https:\/\/github.com\/autowarefoundation\/autoware_universe\/tree\/main\/simulator\/autoware_carla_interface. Accessed: October 2025."},{"key":"e_1_3_1_11_2","unstructured":"[n. d.]. An URL for bridge implementation for Autoware f6b14ec and eeed846. https:\/\/github.com\/evshary\/autoware_carla_launch. Accessed: October 2025."},{"key":"e_1_3_1_12_2","unstructured":"[n. d.]. An URL for descrbing the issue related to traffic light recognition of Autoware. https:\/\/github.com\/autowarefoundation\/autoware_universe\/tree\/0.39.0\/simulator\/autoware_carla_interface#known-issues-and-future-works. Accessed: October 2025."},{"key":"e_1_3_1_13_2","unstructured":"[n. d.]. An URL for Random Forest Regressor from scikit-learn library. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.RandomForestRegressor.html. Accessed: October 2025."},{"key":"e_1_3_1_14_2","unstructured":"[n. d.]. An URL for Stanley. https:\/\/cs.stanford.edu\/group\/roadrunner\/\/old\/index.html. Accessed: October 2025."},{"key":"e_1_3_1_15_2","unstructured":"[n. d.]. An URL for carla.BoundingBox. https:\/\/carla.readthedocs.io\/en\/0.9.15\/python_api\/#carlaboundingbox. Accessed: October 2025."},{"key":"e_1_3_1_16_2","unstructured":"[n. d.]. An URL for the artifact of Prunario (GitHub). https:\/\/github.com\/kupl\/Prunario-public. Accessed: February 2026."},{"key":"e_1_3_1_17_2","unstructured":"[n.d.]. An URL for the artifact of SAMOTA. https:\/\/figshare.com\/articles\/journal_contribution\/Replication_Package_For_Efficient_Online_Testing_for_DNN-based_Systems_using_Surrogate-Assisted_and_Many-Objective_Optimization_\/16468530?file=34050833. Accessed: October 2025."},{"key":"e_1_3_1_18_2","unstructured":"[n. d.]. An URL for the artifact of ScenarioFuzz. https:\/\/github.com\/AtongWang\/ScenarioFuzz\/tree\/49360794846786b133799a62827528bc4644bc62. Accessed: October 2025."},{"key":"e_1_3_1_19_2","unstructured":"[n. d.]. An URL for the artifact of DriveFuzz. https:\/\/gitlab.com\/s3lab-code\/public\/drivefuzz\/-\/tree\/ae08cc66d6fe0f8bc67ff2d3de55ab3f26809b59. Accessed: October 2025."},{"key":"e_1_3_1_20_2","unstructured":"[n. d.]. An URL for the default max speed of Autoware. https:\/\/github.com\/autowarefoundation\/autoware_launch\/blob\/c445de532fc373aadb43cdb37d92f7109073320a\/autoware_launch\/config\/planning\/scenario_planning\/common\/common.param.yaml#L3. Accessed: October 2025."},{"key":"e_1_3_1_21_2","unstructured":"[n. d.]. An URL for the frame time of ScenarioFuzz. https:\/\/github.com\/AtongWang\/ScenarioFuzz\/blob\/49360794846786b133799a62827528bc4644bc62\/src\/constants.py#L69. Accessed: October 2025."},{"key":"e_1_3_1_22_2","unstructured":"[n. d.]. An URL for the frame time of DriveFuzz. https:\/\/gitlab.com\/s3lab-code\/public\/drivefuzz\/-\/blame\/master\/src\/constants.py?ref_type=heads#L35. Accessed: October 2025."},{"key":"e_1_3_1_23_2","unstructured":"[n.d.]. An URL for the method distance in CARLA simulator. https:\/\/carla.readthedocs.io\/en\/0.9.15\/python_api\/#carla.Location.distance. Accessed: October 2025."},{"key":"e_1_3_1_24_2","unstructured":"[n.d.]. An URL for the method get_spawn_points in CARLA simulator. https:\/\/carla.readthedocs.io\/en\/0.9.15\/python_api\/#carla.Map.get_spawn_points. Accessed: October 2025."},{"key":"e_1_3_1_25_2","unstructured":"[n. d.]. An URL for the method get_waypoint in CARLA simulator. https:\/\/carla.readthedocs.io\/en\/0.9.15\/python_api\/#carla.Map.get_waypoint. Accessed: October 2025."},{"key":"e_1_3_1_26_2","unstructured":"[n.d.]. An URL for Waymo Driver. https:\/\/waymo.com\/waymo-driver\/. Accessed: October 2025."},{"key":"e_1_3_1_27_2","unstructured":"[n. d.]. autoware.universe 4a3de49. https:\/\/github.com\/autowarefoundation\/autoware.universe\/tree\/4a3de4915116b5d0e3db50f1976e9dd52edc0e20. Accessed: October 2025."},{"key":"e_1_3_1_28_2","unstructured":"[n. d.]. autoware.universe 75549a6. https:\/\/github.com\/autowarefoundation\/autoware.universe\/tree\/75549a6e8b395d36dcc367ba052559bd40832280. Accessed: October 2025."},{"key":"e_1_3_1_29_2","unstructured":"[n. d.]. autoware.universe 7877192. https:\/\/github.com\/autowarefoundation\/autoware.universe\/tree\/7877192cb5700c1db15109dcd959785fc381951d. Accessed: October 2025."},{"key":"e_1_3_1_30_2","unstructured":"[n. d.]. autoware.universe eeed846. https:\/\/github.com\/autowarefoundation\/autoware.universe\/tree\/eeed8466ab33d316c9e0c2761ccec39f90469e64. Accessed: October 2025."},{"key":"e_1_3_1_31_2","unstructured":"[n.d.]. autoware.universe f6b14ec. https:\/\/github.com\/autowarefoundation\/autoware.universe\/tree\/f6b14ecce6c6aabe962a173e6d82afe2333fc0fa. Accessed: October 2025."},{"key":"e_1_3_1_32_2","unstructured":"[n. d.]. CARLA: Open-source simulator for autonomous driving research. https:\/\/carla.org. Accessed: October 2025."},{"key":"e_1_3_1_33_2","unstructured":"[n. d.]. History of self-driving cars. https:\/\/en.wikipedia.org\/wiki\/History_of_self-driving_cars. Accessed: October 2025."},{"key":"e_1_3_1_34_2","unstructured":"[n. d.]. How GM\u2019s Cruise robotaxi tech failures led it to drag pedestrian 20 feet. https:\/\/www.reuters.com\/business\/autos-transportation\/how-gms-cruise-robotaxi-tech-failuresled-it-drag-pedestrian-20-feet-2024-01-26\/. Accessed: October 2025."},{"key":"e_1_3_1_35_2","unstructured":"[n.d.]. Sensors Reference - Collision Detector. https:\/\/carla.readthedocs.io\/en\/0.9.15\/ref_sensors\/#collision-detector. Accessed: October 2025."},{"key":"e_1_3_1_36_2","unstructured":"[n. d.]. Sensors Reference - Lane Invasion. https:\/\/carla.readthedocs.io\/en\/0.9.15\/ref_sensors\/#lane-invasion-detector. Accessed: October 2025."},{"key":"e_1_3_1_37_2","unstructured":"[n. d.]. Tesla Autopilot feature was involved in 13 fatal crashes US regulator says. https:\/\/www.theguardian.com\/technology\/2024\/apr\/26\/tesla-autopilot-fatal-crash. Accessed: October 2025."},{"key":"e_1_3_1_38_2","unstructured":"[n. d.]. Tesla driver dies in first fatal autonomous car crash in US. https:\/\/www.newscientist.com\/article\/2095740-tesla-driver-dies-in-first-fatal-autonomous-car-crash-in-us\/. Accessed: October 2025."},{"key":"e_1_3_1_39_2","unstructured":"[n. d.]. The Autoware Foundation. https:\/\/autoware.org. Accessed: October 2025."},{"key":"e_1_3_1_40_2","unstructured":"[n. d.]. Town 1. https:\/\/carla.readthedocs.io\/en\/0.9.15\/map_town01\/. Accessed: October 2025."},{"key":"e_1_3_1_41_2","unstructured":"[n. d.]. Town 3. https:\/\/carla.readthedocs.io\/en\/0.9.15\/map_town03\/. Accessed: October 2025."},{"key":"e_1_3_1_42_2","unstructured":"[n. d.]. Town 4. https:\/\/carla.readthedocs.io\/en\/0.9.15\/map_town04\/. Accessed: October 2025."},{"key":"e_1_3_1_43_2","unstructured":"[n. d.]. Town 5. https:\/\/carla.readthedocs.io\/en\/0.9.15\/map_town05\/. Accessed: October 2025."},{"key":"e_1_3_1_44_2","unstructured":"[n. d.]. Uber self-driving car that killed pedestrian had software flaws. https:\/\/www.rac.co.uk\/drive\/news\/motoring-news\/uber-self-driving-car-that-killed-pedestrian-had-software-flaws\/. Accessed: October 2025."},{"key":"e_1_3_1_45_2","unstructured":"[n. d.]. An URL for the attribute lane_width in CARLA simulator. https:\/\/carla.readthedocs.io\/en\/0.9.15\/python_api\/#carla.Waypoint.lane_width. Accessed: October 2025."},{"key":"e_1_3_1_46_2","unstructured":"[n. d.]. An URL for the planning module of Autoware. https:\/\/autowarefoundation.github.io\/autoware.universe\/main\/planning\/behavior_path_planner\/autoware_behavior_path_planner\/. Accessed: October 2025."},{"key":"e_1_3_1_47_2","unstructured":"[n. d.]. Waymo robotaxi accident with San Francisco cyclist draws regulatory review. https:\/\/www.reuters.com\/world\/us\/driverless-waymo-car-hits-cyclist-san-francisco-causes-minor-scratches-2024-02-07\/. Accessed: October 2025."},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1002\/stvr.1486"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/itsc.2017.8317768"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","unstructured":"Raja Ben Abdessalem Shiva Nejati Lionel C Briand and Thomas Stifter. 2016. Testing advanced driver assistance systems using multi-objective search and neural networks. In Proceedings of the 31st IEEE\/ACM international conference on automated software engineering. 63\u201374. doi:10.1145\/2970276.2970311","DOI":"10.1145\/2970276.2970311"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","unstructured":"Samy Bengio Oriol Vinyals Navdeep Jaitly and Noam Shazeer. 2015. Scheduled sampling for sequence prediction with recurrent neural networks. Advances in neural information processing systems 28 (2015). doi:10.48550\/arXiv.1506.03099","DOI":"10.48550\/arXiv.1506.03099"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3631970"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3533818"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1604.07316"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","unstructured":"Lars Buitinck Gilles Louppe Mathieu Blondel Fabian Pedregosa Andreas Mueller Olivier Grisel Vlad Niculae Peter Prettenhofer Alexandre Gramfort Jaques Grobler Robert Layton Jake VanderPlas Arnaud Joly Brian Holt and Ga\u00ebl Varoquaux. 2013. API design for machine learning software: experiences from the scikit-learn project. In ECML PKDD Workshop: Languages for Data Mining and Machine Learning. 108\u2013122. doi:10.48550\/arXiv.1309.0238","DOI":"10.48550\/arXiv.1309.0238"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3660792"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","unstructured":"Mingfei Cheng Yuan Zhou and Xiaofei Xie. 2023. Behavexplor: Behavior diversity guided testing for autonomous driving systems. In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis. 488\u2013500. doi:10.1145\/3597926.3598072","DOI":"10.1145\/3597926.3598072"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549152"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","unstructured":"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. 1620\u20131632. doi:10.1145\/3691620.3695530","DOI":"10.1145\/3691620.3695530"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","unstructured":"Sishuai Gong Dinglan Peng Deniz Altinb\u00fcken Pedro Fonseca and Petros Maniatis. 2023. Snowcat: Efficient kernel concurrency testing using a learned coverage predictor. In Proceedings of the 29th Symposium on Operating Systems Principles. 35\u201351. doi:10.1145\/3600006.3613148","DOI":"10.1145\/3600006.3613148"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","unstructured":"Jiawei Tyler Gu Xudong Sun Wentao Zhang Yuxuan Jiang Chen Wang Mandana Vaziri Owolabi Legunsen and Tianyin Xu. 2023. Acto: Automatic end-to-end testing for operation correctness of cloud system management. In Proceedings of the 29th Symposium on Operating Systems Principles. 96\u2013112. doi:10.1145\/3600006.3613161","DOI":"10.1145\/3600006.3613161"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","unstructured":"Fitash Ul Haq Donghwan Shin and Lionel Briand. 2022. Efficient online testing for DNN-enabled systems using surrogate-assisted and many-objective optimization. In Proceedings of the 44th international conference on software engineering. 811\u2013822. doi:10.1145\/3510003.3510188","DOI":"10.1145\/3510003.3510188"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2106.00873"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/tse.2023.3309610"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/icse48619.2023.00216"},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","unstructured":"Dae R Jeong Yewon Choi Byoungyoung Lee Insik Shin and Youngjin Kwon. 2024. OZZ: Identifying Kernel Out-of-Order Concurrency Bugs with In-Vivo Memory Access Reordering. In Proceedings of the ACM SIGOPS 30th Symposium on Operating Systems Principles. 229\u2013248. doi:10.1145\/3694715.3695944","DOI":"10.1145\/3694715.3695944"},{"key":"e_1_3_1_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00016"},{"key":"e_1_3_1_68_2","unstructured":"Zu-Ming Jiang Si Liu Manuel Rigger and Zhendong Su. 2023. Detecting transactional bugs in database engines via {graph-based} oracle construction. In 17th USENIX Symposium on Operating Systems Design and Implementation (OSDI 23). 397\u2013417."},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","unstructured":"Zu-Ming Jiang and Zhendong Su. 2024. Detecting logic bugs in database engines via equivalent expression transformation. In 18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24). 821\u2013835. doi:10.5555\/3691938.3691982","DOI":"10.5555\/3691938.3691982"},{"key":"e_1_3_1_70_2","unstructured":"Yiru Jiao. 2023. A fast calculation of two-dimensional Time-to-Collision. https:\/\/github.com\/Yiru-Jiao\/Two-Dimensional-Time-To-Collision. Accessed: October 2025."},{"key":"e_1_3_1_71_2","doi-asserted-by":"publisher","unstructured":"Jongwook Kim Sunbeom So and Hakjoo Oh. 2023. Diver: Oracle-Guided SMT Solver Testing with Unrestricted Random Mutations. In 2023 IEEE\/ACM 45th International Conference on Software Engineering (ICSE). 2224\u20132236. doi:10.1109\/ICSE48619.2023.00187","DOI":"10.1109\/ICSE48619.2023.00187"},{"key":"e_1_3_1_72_2","doi-asserted-by":"publisher","unstructured":"Minsu Kim. 2026. Prunario: Testing Autonomous Driving Systems by Pruning Likely Redundant Scenarios. doi:10.5281\/zenodo.18810329","DOI":"10.5281\/zenodo.18810329"},{"key":"e_1_3_1_73_2","doi-asserted-by":"publisher","unstructured":"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. 1753\u20131767. doi:10.1145\/3548606.3560558","DOI":"10.1145\/3548606.3560558"},{"key":"e_1_3_1_74_2","doi-asserted-by":"publisher","DOI":"10.1109\/icra46639.2022.9811794"},{"key":"e_1_3_1_75_2","doi-asserted-by":"publisher","DOI":"10.1109\/issre5003.2020.00012"},{"key":"e_1_3_1_76_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2401.11876"},{"key":"e_1_3_1_77_2","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549111"},{"key":"e_1_3_1_78_2","doi-asserted-by":"publisher","unstructured":"You Lu Yifan Tian Yuyang Bi Bihuan Chen and Xin Peng. 2024. DiaVio: LLM-Empowered Diagnosis of Safety Violations in ADS Simulation Testing. In Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis. 376\u2013388. doi:10.1145\/3650212.3652135","DOI":"10.1145\/3650212.3652135"},{"key":"e_1_3_1_79_2","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177730491"},{"key":"e_1_3_1_80_2","doi-asserted-by":"publisher","DOI":"10.1002\/rob.20258"},{"key":"e_1_3_1_81_2","doi-asserted-by":"publisher","DOI":"10.1109\/aitest52744.2021.00033"},{"key":"e_1_3_1_82_2","doi-asserted-by":"publisher","unstructured":"Kexin Pei Yinzhi Cao Junfeng Yang and Suman Jana. 2017. Deepxplore: Automated whitebox testing of deep learning systems. In proceedings of the 26th Symposium on Operating Systems Principles. 1\u201318. doi:10.1145\/3132747.3132785","DOI":"10.1145\/3132747.3132785"},{"key":"e_1_3_1_83_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2005.03778"},{"key":"e_1_3_1_84_2","doi-asserted-by":"publisher","unstructured":"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\u201313. doi:10.1145\/3597503.3639175","DOI":"10.1145\/3597503.3639175"},{"key":"e_1_3_1_85_2","doi-asserted-by":"publisher","unstructured":"Maolin Sun Yibiao Yang Ming Wen Yongcong Wang Yuming Zhou and Hai Jin. 2023. Validating SMT Solvers via Skeleton Enumeration Empowered by Historical Bug-Triggering Inputs. In 2023 IEEE\/ACM 45th International Conference on Software Engineering (ICSE). 69\u201381. doi:10.1109\/ICSE48619.2023.00018","DOI":"10.1109\/ICSE48619.2023.00018"},{"key":"e_1_3_1_86_2","unstructured":"Xudong Sun Wenqing Luo Jiawei Tyler Gu Aishwarya Ganesan Ramnatthan Alagappan Michael Gasch Lalith Suresh and Tianyin Xu. 2022. Automatic reliability testing for cluster management controllers. In 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22). 143\u2013159."},{"key":"e_1_3_1_87_2","doi-asserted-by":"publisher","unstructured":"Shuncheng Tang Zhenya Zhang Jixiang Zhou Lei Lei Yuan Zhou and Yinxing Xue. 2024. LeGEND: A Top-Down Approach to Scenario Generation of Autonomous Driving Systems Assisted by Large Language Models. In Proceedings of the 39th IEEE\/ACM International Conference on Automated Software Engineering. 1497\u20131508. doi:10.1145\/3691620.3695520","DOI":"10.1145\/3691620.3695520"},{"key":"e_1_3_1_88_2","doi-asserted-by":"publisher","DOI":"10.1109\/iv48863.2021.9575536"},{"key":"e_1_3_1_89_2","doi-asserted-by":"publisher","DOI":"10.1109\/ase51524.2021.9678735"},{"key":"e_1_3_1_90_2","doi-asserted-by":"publisher","unstructured":"Haoxiang Tian Yan Jiang Guoquan Wu Jiren Yan Jun Wei Wei Chen Shuo Li and Dan Ye. 2022. MOSAT: finding safety violations of autonomous driving systems using multi-objective genetic algorithm. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 94\u2013106. doi:10.1145\/3540250.3549100","DOI":"10.1145\/3540250.3549100"},{"key":"e_1_3_1_91_2","doi-asserted-by":"publisher","unstructured":"Yuchi Tian Kexin Pei Suman Jana and Baishakhi Ray. 2018. Deeptest: Automated testing of deep-neural-network-driven autonomous cars. In Proceedings of the 40th international conference on software engineering. 303\u2013314. doi:10.1145\/3180155.3180220","DOI":"10.1145\/3180155.3180220"},{"key":"e_1_3_1_92_2","doi-asserted-by":"publisher","DOI":"10.2307\/1165329"},{"key":"e_1_3_1_93_2","doi-asserted-by":"publisher","DOI":"10.1145\/3650212.3680344"},{"key":"e_1_3_1_94_2","unstructured":"Eric W Weisstein. 2004. Bonferroni correction. https:\/\/mathworld.wolfram.com\/ (2004)."},{"key":"e_1_3_1_95_2","doi-asserted-by":"publisher","DOI":"10.1145\/3689795"},{"key":"e_1_3_1_96_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00017"},{"key":"e_1_3_1_97_2","doi-asserted-by":"publisher","unstructured":"Mengshi Zhang Yuqun Zhang Lingming Zhang Cong Liu and Sarfraz Khurshid. 2018. Deeproad: Gan-based metamorphic testing and input validation framework for autonomous driving systems. In Proceedings of the 33rd ACM\/IEEE international conference on automated software engineering. 132\u2013142. doi:10.1145\/3238147.3238187","DOI":"10.1145\/3238147.3238187"},{"key":"e_1_3_1_98_2","doi-asserted-by":"publisher","DOI":"10.1109\/tse.2022.3195640"},{"key":"e_1_3_1_99_2","doi-asserted-by":"publisher","unstructured":"Husheng Zhou Wei Li Zelun Kong Junfeng Guo Yuqun Zhang Bei Yu Lingming Zhang and Cong Liu. 2020. Deepbillboard: Systematic physical-world testing of autonomous driving systems. In Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering. 347\u2013358. doi:10.1145\/3377811.3380422","DOI":"10.1145\/3377811.3380422"},{"key":"e_1_3_1_100_2","doi-asserted-by":"publisher","DOI":"10.1145\/3664605"}],"container-title":["Proceedings of the ACM on Programming Languages"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3798262","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T11:21:36Z","timestamp":1790421696000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3798262"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,10]]},"references-count":99,"journal-issue":{"issue":"OOPSLA1","published-print":{"date-parts":[[2026,4,10]]}},"alternative-id":["10.1145\/3798262"],"URL":"https:\/\/doi.org\/10.1145\/3798262","relation":{},"ISSN":["2475-1421"],"issn-type":[{"value":"2475-1421","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,10]]},"assertion":[{"value":"2025-10-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-02-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}