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]
   84   85   86   87   88   89   90   91   92   93   94