Page 118 - 《软件学报》2025年第7期

沈庆超 等: 深度学习编译器缺陷实证研究: 现状与演化分析 3039

[19] Garcia J, Feng Y, Shen JJ, Almanee S, Xia Y, Chen QA. A comprehensive study of autonomous vehicle bugs. In: Proc. of the 42nd
ACM/IEEE Int’l Conf. on Software Engineering. Seoul: ACM, 2020. 385–396. [doi: 10.1145/3377811.3380397]
[20] 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]
[21] Islam J, Nguyen G, Pan R, Rajan H. A comprehensive study on deep learning bug characteristics. In: Proc. of the 27th ACM Joint
Meeting on European Software Engineering Conf. and Symp. on the Foundations of Software Engineering. Tallinn: ACM, 2019.
510–520. [doi: 10.1145/3338906.3338955]
[22] Zhang YH, Chen YF, Cheung SC, Xiong YF, Zhang L. An empirical study on TensorFlow program bugs. In: Proc. of the 27th ACM
SIGSOFT Int’l Symp. on Software Testing and Analysis. Amsterdam: ACM, 2018. 129–140. [doi: 10.1145/3213846.3213866]
[23] Abadi M, Agarwal A, Barham P, et al. TensorFlow: Large-scale machine learning on heterogeneous distributed systems. arXiv:1603.
04467, 2016.
[24] Lu S, Park S, Seo E, and Zhou YY. Learning from mistakes: A comprehensive study on real world concurrency bug characteristics. In:
Proc. of the 13th Int’l Conf. on Architectural Support for Programming Languages and Operating Systems. Washington: ACM, 2008.
329–339. [doi: 10.1145/1346281.1346323]
[25] Di Franco A, Guo H, Rubio-González C. A comprehensive study of real-world numerical bug characteristics. In: Proc. of the 32nd
IEEE/ACM Int’l Conf. on Automated Software Engineering. Urbana: IEEE, 2017. 509–519. [doi: 10.1109/ASE.2017.8115662]
[26] Han X, Yu TT. An empirical study on performance bugs for highly configurable software systems. In: Proc. of the 10th ACM/IEEE Int’l
Symp. on Empirical Software Engineering and Measurement. Ciudad Real: ACM, 2016. 23. [doi: 10.1145/2961111.2962602]
[27] Wan ZY, Lo D, Xia X, Cai L. Bug characteristics in blockchain systems: A large-scale empirical study. In: Proc. of the 14th IEEE/ACM
Int’l Conf. on Mining Software Repositories. Buenos Aires: IEEE, 2017. 413–424. [doi: 10.1109/MSR.2017.59]
[28] Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin ZM, Gimelshein N, Antiga L, Desmaison A, Köpf A, Yang
E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai JJ, Chintala S. PyTorch: An imperative style, high-
performance deep learning library. In: Proc. of the 33rd Conf. on Neural Information Processing Systems. Vancouver: ACM, 2019. 32.
[29] Moolayil J. Learn Keras for Deep Neural Networks. Berkeley: Apress, 2019. [doi: 10.1007/978-1-4842-4240-7]
[30] Tong ZH, Du N, Song XB, Wang XL. Study on MindSpore deep learning framework. In: Proc. of the 17th Int’l Conf. on Computational
Intelligence and Security. Chengdu: IEEE, 2021. 183–186. [doi: 10.1109/CIS54983.2021.00046]
[31] Li ZM, Tan L, Wang XH, Lu S, Zhou YY, Zhai CX. Have things changed now? An empirical study of bug characteristics in modern open
source software. In: Proc. of the 1st Workshop on Architectural and System Support for Improving Software Dependability. San Jose:
ACM, 2006. 25–33. [doi: 10.1145/1181309.1181314]
[32] Ma HY, Zhang WQ, Shen QC, Tian YQ, Chen JJ, Cheung SC. Towards understanding the bugs in solidity compiler. In: Proc. of the 33rd
ACM SIGSOFT Int’l Symp. on Software Testing and Analysis. Vienna: ACM, 2024. 1312–1324. [doi: 10.1145/3650212.3680362]
[33] Nikanjam A, Morovati MM, Khomh F, Ben Braiek H. Faults in deep reinforcement learning programs: A taxonomy and a detection
approach. Automated Software Engineering, 2022, 29(1): 8. [doi: 10.1007/s10515-021-00313-x]
[34] Shull F, Godfrey S, Bechtel A, Feldmann RL, Regardie M, Seaman C. Making use of a decade of widely varying historical data: SARP
Project—“full life-cycle defect management”. Fraunhofer USA Inc., 2008.
[35] Tan L, Liu C, Li ZM, Wang XH, Zhou YY, Zhai CX. Bug characteristics in open source software. Empirical Software Engineering, 2014,
19(6): 1665–1705. [doi: 10.1007/s10664-013-9258-8]
[36] Thung F, Wang SW, Lo D, Jiang LX. An empirical study of bugs in machine learning systems. In: Proc. of the 23rd IEEE Int’l Symp. on
Software Reliability Engineering. Dallas: IEEE, 2012. 271–280. [doi: 10.1109/ISSRE.2012.22]
[37] Vieira SM, Kaymak U, Sousa JMC. Cohen’s Kappa coefficient as a performance measure for feature selection. In: Proc. of the 2010 Int’l
Conf. on Fuzzy Systems. Barcelona: IEEE, 2010. 1–8. [doi: 10.1109/FUZZY.2010.5584447]
[38] Yang CY, Deng YL, Lu RY, Yao JY, Liu JW, Jabbarvand R, Zhang LM. WhiteFox: White-box compiler fuzzing empowered by large
language models. Proc. of the ACM on Programming Languages, 2024, 8(OOPSLA2): 296. [doi: 10.1145/3689736]
[39] Shen QC, Tian YQ, Ma HY, Chen JJ, Huang LL, Fu RF, Cheung SC, Wang Z. A tale of two DL cities: When library tests meet compiler.
arXiv:2407.16626, 2024.
[40] Liu JW, Wei YX, Yang S, Deng YL, Zhang LM. Coverage-guided tensor compiler fuzzing with joint IR-pass mutation. Proc. of the ACM
on Programming Languages, 2022, 6(OOPSLA1): 73. [doi: 10.1145/3527317]
[41] Liu JW, Lin JK, Ruffy F, Tan C, Li JY, Panda A, Zhang LM. NNSmith: Generating diverse and valid test cases for deep learning
compilers. In: Proc. of the 28th ACM Int’l Conf. on Architectural Support for Programming Languages and Operating Systems.
Vancouver: ACM, 2023. 530–543. [doi: 10.1145/3575693.3575707]