Page 89 - 《软件学报》2026年第6期
P. 89
2408 软件学报 2026 年第 37 卷第 6 期
workloads. Journal of Computer Science and Technology, 2023, 38(4): 807–820. [doi: 10.1007/s11390-023-1266-6]
[7] Gu DD, Shi YN, Liu XZ, Wu G, Jiang HO, Zhao YS, Ma Y. Defect detection for deep learning frameworks based on meta operators.
Chinese Journal of Computers, 2022, 45(2): 240–255 (in Chinese with English abstract). [doi: 10.11897/SP.J.1016.2022.00240]
[8] Zhang XF, Sun N, Fang CR, Liu JW, Liu J, Chai D, Wang J, Chen ZY. Predoo: Precision testing of deep learning operators. In: Proc. of
the 30th ACM SIGSOFT Int’l Symp. on Software Testing and Analysis. ACM, 2021. 400–412. [doi: 10.1145/3460319.3464843]
[9] Liu JW, Huang YH, Wang ZJ, Ma L, Fang CR, Gu MZ, Zhang XF, Chen ZY. Generation-based differential fuzzing for deep learning
libraries. ACM Trans. on Software Engineering and Methodology, 2023, 33(2): 50. [doi: 10.1145/3628159]
[10] Ou SW, Li YW, Yu L, Wei CK, Wen TK, Chen QP, Chen Y, Tang HZ, Pan ZL. MirrorFuzz: Leveraging LLM and shared bugs for deep
learning framework APIs fuzzing. IEEE Trans. on Software Engineering, 2025, 52(1): 360–375. [doi: 10.1109/TSE.2025.3619966]
[11] Lee JKL, Jamieson M, Brown N, Jesus R. Test-driving RISC-V vector hardware for HPC. In: Proc. of the 2023 ISC High Performance Int’l
Workshops on High Performance Computing. Hamburg: Springer, 2023. 419–432. [doi: 10.1007/978-3-031-40843-4_31]
[12] Xu XZ, Yang DH, Wang L, Wang T, Huang AW, Li Q. Sequential consistency per location theorem proving in RISC-V memory
consistency model. Ruan Jian Xue Bao/Journal of Software, 2025, 36(9): 3919–3936 (in Chinese with English abstract). http://www.jos.
org.cn/1000-9825/7292.htm [doi: 10.13328/j.cnki.jos.007292]
[13] Li CD, Yi R, Luo YW, Wang XL, Wang ZL. Lazy shadow paging under the RISC-V architecture. Ruan Jian Xue Bao/Journal of
Software, 2025, 36(9): 3970–3984 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/7359.htm [doi: 10.13328/j.cnki.
jos.007359]
[14] Poorhosseini M, Nebel W, Grüttner K. A compiler comparison in the RISC-V ecosystem. In: Proc. of the 2020 Int’l Conf. on Omni-layer
Intelligent Systems (COINS). Barcelona: IEEE, 2020. 1–6. [doi: 10.1109/COINS49042.2020.9191411]
[15] Adit N, Sampson A. Performance left on the table: An evaluation of compiler autovectorization for RISC-V. IEEE Micro, 2022, 42(5):
41–48. [doi: 10.1109/MM.2022.3184867]
[16] Bjäreholt J. RISC-V compiler performance: A comparison between GCC and LLVM/Clang [BS. Thesis]. Karlskrona: Blekinge Institute
of Technology, 2017.
[17] Zhang XF, Liu JW, Sun N, Fang CR, Liu J, Wang J, Chai D, Chen ZY. Duo: Differential fuzzing for deep learning operators. IEEE
Trans. on Reliability, 2021, 70(4): 1671–1685. [doi: 10.1109/TR.2021.3107165]
[18] Wen ZZ, Liu HY, Zhu TW, Pan MX, Wang SH, Lin YY, Liu KR, Zhang T, Li XD. A study of floating-point precision tuning in deep
learning operators implementations. ACM Trans. on Software Engineering and Methodology, 2025. [doi: 10.1145/3773992]
[19] Ma XY, Du XT, Cai Q, Zheng Y, Hu Z, Zheng Z. Survey on testing of deep learning frameworks. Ruan Jian Xue Bao/Journal of
Software, 2024, 35(8): 3752–3784 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/7059.htm [doi: 10.13328/j.cnki.
jos.007059]
[20] Zhang XY, Jiang WP, Shen C, Li Q, Wang Q, Lin CH, Guan XH. Deep learning library testing: Definition, methods and challenges.
ACM Computing Surveys, 2025, 57(7): 187. [doi: 10.1145/3716497]
[21] Liu JW, Zhang XF, Xu LR, Fang CR, Gu MZ, Luo WS, Chai D, Wang J, Zhao ZH, Chen ZY. Automated detection and repair of floating-
point precision problems in convolutional neural network operators. ACM Trans. on Software Engineering and Methodology, 2025,
34(7): 203. [doi: 10.1145/3715104]
[22] Shi JY, Xiao Y, Li YK, Li YT, Yu DS, Yu CD, Su H, Chen YF, Huo W. ACETest: Automated constraint extraction for testing deep
learning operators. In: Proc. of the 32nd ACM SIGSOFT Int’l Symp. on Software Testing and Analysis. Seattle: ACM, 2023. 690–702.
[doi: 10.1145/3597926.3598088]
[23] Chen JY, Jia CY, Yan YJ, Ge J, Zheng HB, Cheng Y. A miss is as good as a mile: Metamorphic testing for deep learning operators. Proc.
of the ACM on Software Engineering, 2024, 1(FSE): 89. [doi: 10.1145/3660796]
[24] Chen JJ, Liang YH, Shen QC, Jiang JJ, Li SC. Toward understanding deep learning framework bugs. ACM Trans. on Software
Engineering and Methodology, 2023, 32(6): 135. [doi: 10.1145/3587155]
[25] Ge J, Yu HQ, Fan GS, Tang JH, Huang ZJ. Just-in-time defect prediction for intelligent computing frameworks. Ruan Jian Xue
Bao/Journal of Software, 2023, 34(9): 3966–3980 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6874.htm [doi: 10.
13328/j.cnki.jos.006874]
[26] Wei AJ, Deng YL, Yang CY, Zhang LM. Free lunch for testing: Fuzzing deep-learning libraries from open source. In: Proc. of the 44th
Int’l Conf. on Software Engineering. Pittsburgh: ACM, 2022. 995–1007. [doi: 10.1145/3510003.3510041]
[27] Pham HV, Lutellier T, Qi WZ, Tan L. CRADLE: Cross-backend validation to detect and localize bugs in deep learning libraries. In: Proc.
of the 41st IEEE/ACM Int’l Conf. on Software Engineering (ICSE). Montreal: IEEE, 2019. 1027–1038. [doi: 10.1109/ICSE.2019.00107]

