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[52] Tian YC, Pei KX, Jana S, Ray B. DeepTest: Automated testing of deep-neural-network-driven autonomous cars. In: Proc. of the 40th Int’l
Conf. on Software Engineering. Gothenburg: ACM, 2018. 303–314. [doi: 10.1145/3180155.3180220]
[53] Wang S, Su ZD. Metamorphic object insertion for testing object detection systems. In: Proc. of the 35th IEEE/ACM Int’l Conf. on
Automated Software Engineering. ACM, 2021. 1053–1065. [doi: 10.1145/3324884.3416584]
[54] Shao JY. Testing object detection for autonomous driving systems via 3D reconstruction. In: Proc. of the 43rd IEEE/ACM Int’l Conf. on
Software Engineering: Companion Proc. (ICSE-Companion). Madrid: IEEE, 2021. 117–119. [doi: 10.1109/ICSE-Companion52605.
2021.00052]
[55] Wang XL, Yang SQ, Shao JY, Chang J, Gao G, Li M, Xuan JF. Object removal for testing object detection in autonomous vehicle
systems. In: Proc. of the 21st IEEE Int’l Conf. on Software Quality, Reliability and Security Companion (QRS-C). Haikou: IEEE, 2021.
543–549. [doi: 10.1109/qrs-c55045.2021.00083]
[56] Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. Journal of Big Data, 2019, 6(1): 60. [doi: 10.
1186/s40537-019-0197-0]
[57] Dasiopoulou S, Mezaris V, Kompatsiaris I, Papastathis VK, Strintzis MG. Knowledge-assisted semantic video object detection. IEEE
Trans. on Circuits and Systems for Video Technology, 2005, 15(10): 1210–1224 (in Chinese with English abstract). [doi: 10.1109/
TCSVT.2005.854238]
[58] Yuan L, Li XM, Pan ZX, Sun JM, Xiao L. Review of adversarial examples for object detection. Journal of Image and Graphics, 27(10):
2873–2896. [doi: 10.11834/jig.210209]
[59] Liu L, Ouyang WL, Wang XG, Fieguth P, Chen J, Liu XW, Pietikäinen M. Deep learning for generic object detection: A survey. Int’l
Journal of Computer Vision, 2020, 128(2): 261–318. [doi: 10.1007/s11263-019-01247-4]
[60] Xie C, Wang JY, Zhang ZS, Zhou YY, Xie LX, Yuille A. Adversarial examples for semantic segmentation and object detection. In:
Proc. of the 2017 IEEE Int’l Conf. on Computer Vision (ICCV). Venice: IEEE, 2017. 1378–1387. [doi: 10.1109/ICCV.2017.153]
[61] Otter DW, Medina JR, Kalita JK. A survey of the usages of deep learning for natural language processing. IEEE Trans. on Neural
Networks and Learning Systems, 2021, 32(2): 604–624. [doi: 10.1109/TNNLS.2020.2979670]
[62] Liu ZX, Feng Y, Chen ZY. DialTest: Automated testing for recurrent-neural-network-driven dialogue systems. In: Proc. of the 30th
ACM SIGSOFT Int’l Symp. on Software Testing and Analysis. ACM, 2021. 115–126. [doi: 10.1145/3460319.3464829]
[63] Shen XC, Chen HB, Chen JF, Zhang JW, Wang SH. EcoDialTest: Adaptive mutation schedule for automated dialogue systems testing.
In: Proc. of the 2023 IEEE Int’l Conf. on Software Analysis, Evolution and Reengineering (SANER). Taipa: IEEE, 2023. 933–939.
[doi: 10.1109/SANER56733.2023.00113]
[64] Chen HB, Chen JF, Wu YC, Cai SH, Ahmad B, Huang RB, Wang SR, Zhang C. DialTest-EA: An enhanced fuzzing approach with
energy adjustment for dialogue systems via metamorphic testing. Software Testing, Verification and Reliability, 2025, 35(1): e1897.
[doi: 10.1002/stvr.1897]
[65] Guo GX, Aleti A, Neelofar N, Tantithamthavorn C. MORTAR: Metamorphic multi-turn testing for LLM-based dialogue systems.
arXiv:2412.15557, 2024.
[66] He PJ, Meister C, Su ZD. Structure-invariant testing for machine translation. In: Proc. of the 42nd ACM/IEEE Int’l Conf. on Software
Engineering. Seoul: ACM, 2020. 961–973. [doi: 10.1145/3377811.3380339]
[67] He PJ, Meister C, Su ZD. Testing machine translation via referential transparency. In: Proc. of the 43rd IEEE/ACM Int’l Conf. on
Software Engineering. Madrid: IEEE, 2021. 410–422. [doi: 10.1109/ICSE43902.2021.00047]
[68] Gupta S, He PJ, Meister C, Su ZD. Machine translation testing via pathological invariance. In: Proc. of the 28th ACM Joint Meeting
European Software Engineering Conf. and Symp. on the Foundations of Software Engineering. ACM, 2020. 863–875. [doi: 10.1145/
3368089.3409756]
[69] Pesu D, Zhou ZQ, Zhen JF, Towey D. A Monte Carlo method for metamorphic testing of machine translation services. In: Proc. of the
3rd Int’l Workshop on Metamorphic Testing. Gothenburg: ACM, 2018. 38–45. [doi: 10.1145/3193977.3193980]
[70] Zheng WJ, Wang WY, Liu D, Zhang CR, Zeng QS, Deng YT, Yang W, He PJ, Xie T. Testing untestable neural machine translation: An
industrial case. In: Proc. of the 41st IEEE/ACM Int’l Conf. on Software Engineering: Companion Proc. (ICSE-companion). Montreal:
IEEE, 2019. 314–315. [doi: 10.1109/ICSE-Companion.2019.00131]
[71] Sun ZY, Zhang JM, Harman M, Papadakis M, Zhang L. Automatic testing and improvement of machine translation. In: Proc. of the
42nd ACM/IEEE Int’l Conf. on Software Engineering. Seoul: ACM, 2020. 974–985. [doi: 10.1145/3377811.3380420]
[72] Sun ZY, Zhang JM, Xiong YF, Harman M, Papadakis M, Zhang L. Improving machine translation systems via isotopic replacement. In:
Proc. of the 44th Int’l Conf. on Software Engineering. Pittsburgh: ACM, 2022. 1181–1192. [doi: 10.1145/3510003.3510206]
[73] Lee DTS, Zhou ZQ, Tse TH. Metamorphic robustness testing of Google translate. In: Proc. of the 42nd IEEE/ACM Int’l Conf. on