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                  [7]   Lu S, Guo DY, Ren S, Huang JJ, Svyatkovskiy A, Blanco A, Clement C, Drain D, Jiang DX, Tang DY, Li G, Zhou LD, Shou LJ, Zhou
                      L, Tufano M, Gong M, Zhou M, Duan N, Sundaresan N, Deng SK, Fu SY, Liu SJ. CodexGLUE: A machine learning benchmark dataset
                      for code understanding and generation. arXiv:2102.04664, 2021.
                  [8]   Zhou YQ, Liu SQ, Siow J, Du XN, Liu Y. Devign: Effective vulnerability identification by learning comprehensive program semantics
                      via graph neural networks. In: Proc. of the 33rd Int’l Conf. on Neural Information Processing Systems. Red Hook: Curran Associates
                      Inc. , 2019. 10197–10207.
                  [9]   White  M,  Tufano  M,  Vendome  C,  Poshyvanyk  D.  Deep  learning  code  fragments  for  code  clone  detection.  In:  Proc.  of  the  31st
                      IEEE/ACM Int’l Conf. on Automated Software Engineering. Singapore: IEEE, 2016. 87–98. [doi: ]
                 [10]   Ahmad W, Chakraborty S, Ray B, Chang KW. A Transformer-based approach for source code summarization. In: Proc. of the 58th
                      Annual Meeting of the Association for Computational Linguistics. ACL, 2020. 4998–5007. [doi: 10.18653/v1/2020.acl-main.449]
                 [11]   Lu ZP, Hu HC, Huo SM, Li SY. Ensemble learning methods of adversarial attacks and defenses in computer vision: Recent progress. In:
                      Proc. of the 2021 Int’l Conf. on Advanced Computing and Endogenous Security. Nanjing: IEEE, 2022. 1–10. [doi: 10.1109/IEEECONF
                      52377.2022.10013347]
                 [12]   Wang ZB, Guo HC, Zhang ZF, Liu WX, Qin Z, Ren K. Feature importance-aware transferable adversarial attacks. In: Proc. of the 2021
                      IEEE/CVF Int’l Conf. on Computer Vision. Montreal: IEEE, 2021. 7619–7628. [doi: 10.1109/ICCV48922.2021.00754]
                 [13]   Zhang WE, Sheng QZ, Alhazmi A, Li CL. Adversarial attacks on deep-learning models in natural language processing: A survey. ACM
                      Trans. on Intelligent Systems and Technology, 2020, 11(3): 24. [doi: 10.1145/3374217]
                 [14]   Morris  J,  Lifland  E,  Yoo  JY,  Grigsby  J,  Jin  D,  Qi  YJ.  TextAttack:  A  framework  for  adversarial  attacks,  data  augmentation,  and
                      adversarial training in NLP. In: Proc. of the 2020 Conf. on Empirical Methods in Natural Language Processing: System Demonstrations.
                      ACL, 2020. 119–126. [doi: 10.18653/v1/2020.emnlp-demos.16]
                 [15]   Tian Z, Chen JJ, Jin Z. Code difference guided adversarial example generation for deep code models. In: Proc. of the 38th IEEE/ACM
                      Int’l Conf. on Automated Software Engineering. Luxembourg: IEEE, 2023. 850–862. [doi: 10.1109/ASE56229.2023.00149]
                 [16]   Du XH, Wen M, Wei ZC, Wang SW, Jin H. An extensive study on adversarial attack against pre-trained models of code. In: Proc. of the
                      31st ACM Joint European Software Engineering Conf. and Symp. on the Foundations of Software Engineering. San Francisco: ACM,
                      2023. 489–501. [doi: 10.1145/3611643.3616356]
                 [17]   Cao XJ, Chen JJ, Yan M, You HM, Wu Z, Wang Z. Test case selection for neural network via data mutation. Ruan Jian Xue Bao/
                      Journal of Software, 2024, 35(11): 4973–4992 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/7005.htm [doi: 10.
                      13328/j.cnki.jos.007005]
                 [18]   Zhang HZ, Li Z, Li G, Ma L, Liu Y, Jin Z. Generating adversarial examples for holding robustness of source code processing models.
                      In: Proc. of the 2020 AAAI Conf. on Artificial Intelligence. New York: AAAI Press, 2020. 1169–1176. [doi: 10.1609/aaai.v34i01.5469]
                 [19]   Yang Z, Shi JK, He JD, Lo D. Natural attack for pre-trained models of code. In: Proc. of the 44th Int’l Conf. on Software Engineering.
                      Pittsburgh: ACM, 2022. 1482–1493. [doi: 10.1145/3510003.3510146]
                 [20]   Zhang HZ, Fu ZY, Li G, Ma L, Zhao ZH, Yang HA, Sun YZ, Liu Y, Jin Z. Towards robustness of deep program processing models—
                      Detection,  estimation,  and  enhancement.  ACM  Trans.  on  Software  Engineering  and  Methodology,  2022,  31(3):  50.  [doi:  10.1145/
                      3511887]
                 [21]   Zeng ZR, Tan HZ, Zhang HT, Li J, Zhang YQ, Zhang LM. An extensive study on pre-trained models for program understanding and
                      generation. In: Proc. of the 31st ACM SIGSOFT Int’l Symp. on Software Testing and Analysis. ACM, 2022. 39–51. [doi: 10.1145/
                      3533767.3534390]
                 [22]   Karmakar A, Robbes R. What do pre-trained code models know about code? In: Proc. of the 36th IEEE/ACM Int’l Conf. on Automated
                      Software Engineering. Melbourne: IEEE, 2021. 1332–1336. [doi: 10.1109/ASE51524.2021.9678927]
                 [23]   Shi  ES,  Wang  YL,  Zhang  HY,  Du  L,  Han  S,  Zhang  DM,  Sun  HB.  Towards  efficient  fine-tuning  of  pre-trained  code  models:  An
                      experimental study and beyond. In: Proc. of the 32nd ACM SIGSOFT Int’l Symp. on Software Testing and Analysis. Seattle: ACM,
                      2023. 39–51. [doi: 10.1145/3597926.3598036]
                 [24]   Zhu QH, Liang QY, Sun ZY, Xiong YF, Zhang L, Cheng SY. GrammarT5: Grammar-integrated pretrained encoder-decoder neural
                      model  for  code.  In:  Proc.  of  the  46th  IEEE/ACM  Int’l  Conf.  on  Software  Engineering.  Lisbon:  ACM,  2024.  1–13.  [doi:  10.1145/
                      3597503.3639125]
                 [25]   Nijkamp E, Pang B, Hayashi H, Tu LF, Wang H, Zhou YB, Savarese S, Xiong CM. CodeGen: An open large language model for code
                      with multi-turn program synthesis. arXiv:2203.13474, 2022.
                 [26]   Lewis M, Liu YH, Goyal N, Ghazvininejad M, Mohamed A, Levy O, Stoyanov V, Zettlemoyer L. BART: Denoising sequence-to-
                      sequence  pre-training  for  natural  language  generation,  translation,  and  comprehension.  In:  Proc.  of  the  58th  Annual  Meeting  of  the
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