Page 247 - 《软件学报》2026年第4期
P. 247
1688 软件学报 2026 年第 37 卷第 4 期
[13] Harman M, Mansouri SA, Zhang YY. Search-based software engineering: Trends, techniques and applications. ACM Computing Surveys
(CSUR), 2012, 45(1): 11. [doi: 10.1145/2379776.2379787]
[14] McMinn P. Search-based software test data generation: A survey. Software Testing, Verification and Reliability, 2004, 14(2): 105–156.
[doi: 10.1002/stvr.294]
[15] Thibeault Q, Anderson J, Chandratre A, Pedrielli G, Fainekos G. PSY-TaLiRo: A Python toolbox for search-based test generation for
cyber-physical systems. arXiv:2106.02200, 2021.
[16] Guo A, Feng Y, Cheng YZ, Chen ZY. Semantic-guided fuzzing for virtual testing of autonomous driving systems. Journal of Systems and
Software, 2024, 212: 112017. [doi: 10.1016/j.jss.2024.112017]
[17] Koren M, Alsaif S, Lee R, Kochenderfer MJ. Adaptive stress testing for autonomous vehicles. In: Proc. of the 2018 IEEE Intelligent
Vehicles Symp. (IV). Changshu: IEEE, 2018. 1–7. [doi: 10.1109/IVS.2018.8500400]
[18] Ben Abdessalem R, Nejati S, Briand LC, Stifter T. Testing advanced driver assistance systems using multi-objective search and neural
networks. In: Proc. of the 31st IEEE/ACM Int’l Conf. on Automated Software Engineering. Singapore: ACM, 2016. 63–74. [doi: 10.1145/
2970276.2970311]
[19] Tian HX, Wu GQ, Yan JR, Jiang Y, Wei J, Chen W, Li S, Ye D. Generating critical test scenarios for autonomous driving systems via
influential behavior patterns. In: Proc. of the 37th IEEE/ACM Int’l Conf. on Automated Software Engineering. Rochester: ACM, 2022.
46. [doi: 10.1145/3551349.3560430]
[20] Onieva E, Hernández-Jayo U, Osaba E, Perallos A, Zhang X. A multi-objective evolutionary algorithm for the tuning of fuzzy rule bases
for uncoordinated intersections in autonomous driving. Information Sciences, 2015, 321: 14–30. [doi: 10.1016/j.ins.2015.05.036]
[21] Klück F, Zimmermann M, Wotawa F, Nica M. Genetic algorithm-based test parameter optimization for ADAS system testing. In: Proc. of
the 19th IEEE Int’l Conf. on Software Quality, Reliability and Security (QRS). Sofia: IEEE, 2019. 418–425. [doi: 10.1109/QRS.2019.
00058]
[22] Li GP, Li YR, Jha S, Tsai T, Sullivan M, Hari SKS, Kalbarczyk Z, Iyer R. AV-Fuzzer: Finding safety violations in autonomous driving
systems. In: Proc. of the 31st IEEE Int’l Symp. on Software Reliability Engineering (ISSRE). Coimbra: IEEE, 2020. 25–36. [doi: 10.1109/
ISSRE5003.2020.00012]
[23] Tian HX, Jiang Y, Wu GQ, Yan JR, Wei J, Chen W, Li S, Ye D. MOSAT: Finding safety violations of autonomous driving systems using
multi-objective genetic algorithm. In: Proc. of the 30th ACM Joint European Software Engineering Conf. and Symp. on the Foundations
of Software Engineering. Singapore: ACM, 2022. 94–106. [doi: 10.1145/3540250.3549100]
[24] Fremont DJ, Dreossi T, Ghosh S, Yue XY, Sangiovanni-Vincentelli AL, Seshia SA. Scenic: A language for scenario specification and
scene generation. In: Proc. of the 2019 ACM SIGPLAN Conf. on Programming Language Design and Implementation. Phoenix: ACM,
2019. 63–78. [doi: 10.1145/3314221.3314633]
[25] Guo A, Zhou Y, Tian HX, Fang CR, Sun YJ, Sun WS, Gao XY, Luu AT, Liu Y, Chen ZY. SoVAR: Build generalizable scenarios from
accident reports for autonomous driving testing. In: Proc. of the 39th IEEE/ACM Int’l Conf. on Automated Software Engineering.
Sacramento: ACM, 2024. 268–280. [doi: 10.1145/3691620.3695037]
[26] An open autonomous driving platform. 2013. https://github.com/ApolloAuto/apollo
[27] Navigant research names Waymo, ford autonomous vehicles, cruise, and Baidu the leading developers of automated driving systems.
2020. https://www.businesswire.com/news/home/20200407005119/en/Navigant-Research-Names-Waymo-Ford-Autonomous-Vehicles
[28] Hersey F. Baidu launches their open platform for autonomous cars—And we got to test it. 2017. https://technode.com/2017/07/05/baidu-
apollo-1-0-autonomous-cars-we-test-it/
[29] Autoware self-driving vehicle on a highway. 2025. https://www.youtube.com/watch?v=npQMzH3jd8
[30] Baidu launches public robotaxi trial operation. 2019. https://www.globenewswire.com/news-release/2019/09/26/1921380/0/en/Baidu-
Launches-Public-Robotaxi-Trial-Operation.html
[31] Menzel T, Bagschik G, Maurer M. Scenarios for development, test and validation of automated vehicles. In: Proc. of the 2018 IEEE
Intelligent Vehicles Symp. (IV). Changshu: IEEE, 2018. 1821–1827. [doi: 10.1109/IVS.2018.8500406]
[32] Shin SY, Nejati S, Sabetzadeh M, Briand LC, Zimmer F. Test case prioritization for acceptance testing of cyber physical systems: A multi-
objective search-based approach. In: Proc. of the 27th ACM SIGSOFT Int’l Symp. on Software Testing and Analysis. Amsterdam: ACM,
2018. 49–60. [doi: 10.1145/3213846.3213852]
[33] Nejati S. Testing cyber-physical systems via evolutionary algorithms and machine learning. In: Proc. of the 12th IEEE/ACM Int’l
Workshop on Search-based Software Testing (SBST). Montreal: IEEE, 2019. 1. [doi: 10.1109/SBST.2019.00008]
[34] Arrieta A, Wang S, Markiegi U, Sagardui G, Etxeberria L. Search-based test case generation for cyber-physical systems. In: Proc. of the
2017 IEEE Congress on Evolutionary Computation (CEC). Donostia: IEEE, 2017. 688–697. [doi: 10.1109/CEC.2017.7969377]

