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                      libraries via FuzzGPT. arXiv:2304.02014, 2023.
                 [22]  Kang S, Yoon J, Yoo S. Large language models are few-shot testers: Exploring LLM-based general bug reproduction. In: Proc. of the
                      45th Int’l Conf. on Software Engineering. Melbourne: IEEE, 2023. 2312–2323. [doi: 10.1109/ICSE48619.2023.00194]
                 [23]  Liu Z, Chen CY, Wang JJ, Chen MZ, Wu BY, Che X, Wang DD, Wang Q. Make LLM a testing expert: Bringing human-like interaction
                      to mobile GUI testing via functionality-aware decisions. arXiv:2310.15780, 2023.
                 [24]  Xia CS, Ding YF, Zhang LM. Revisiting the plastic surgery hypothesis via large language models. arXiv:2303.10494, 2023.
                 [25]  Hou  XY,  Zhao  YJ,  Liu  Y,  Yang  Z,  Wang  KL,  Li  L,  Luo  XP,  Lo  D,  Grundy  J,  Wang  HY.  Large  language  models  for  software
                      engineering: A systematic literature review. arXiv:2308.10620, 2024.
                 [26]  Pan SR, Luo LH, Wang YF, Chen C, Wang JP, Wu XD. Unifying large language models and knowledge graphs: A roadmap. IEEE
                      Trans. on Knowledge and Data Engineering, 2024, 36(7): 3580–3599. [doi: 10.1109/TKDE.2024.3352100]
                 [27]  Yang CY, Deng YL, Lu RY, Yao JY, Liu JW, Jabbarvand R, Zhang LM. WhiteFox: White-box compiler fuzzing empowered by large
                      language models. arXiv:2310.15991, 2024.
                 [28]  Sun ML, Yang YB, Wang Y, Wen M, Jia HX, Zhou YM. SMT solver validation empowered by large pre-trained language models. In:
                      Proc. of the 38th IEEE/ACM Int’l Conf. on Automated Software Engineering (ASE). Luxembourg: IEEE, 2023. 1288–1300. [doi: 10.
                      1109/ASE56229.2023.00180]

                 [29]  Xia CS, Paltenghi M, Le Tian J, Pradel M, Zhang LM. Fuzz4All: Universal fuzzing with large language models. In: Proc. of the 46th
                      IEEE/ACM Int’l Conf. on Software Engineering. Lisbon: ACM, 2024. 126. [doi: 10.1145/3597503.3639121]
                 [30]  Wohlin C. Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: Proc. of the 18th Int’l
                      Conf. on Evaluation and Assessment in Software Engineering. London: ACM, 2014. 38. [doi: 10.1145/2601248.2601268]
                 [31]  Jiang JJ, Chen JJ, Xiong YF. Survey of automatic program repair techniques. Ruan Jian Xue Bao/Journal of Software, 2021, 32(9):
                      2665–2690 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6274.htm [doi: 10.13328/j.cnki.jos.006274]
                 [32]  Xia CS, Zhang LM. Less training, more repairing please: Revisiting automated program repair via zero-shot learning. In: Proc. of the
                      30th ACM Joint European Software Engineering Conf. and Symp. on the Foundations of Software Engineering. Singapore: ACM, 2022.
                      959–971. [doi: 10.1145/3540250.3549101]
                 [33]  OpenAI platform. 2023. https://platform.openai.com
                 [34]  Touvron H, Lavril T, Izacard G, Martinet X, Lachaux MA, Lacroix T, Rozière B, Goyal N, Hambro E, Azhar F, Rodriguez A, Joulin A,
                      Grave E, Lample G. LLaMA: Open and efficient foundation language models. arXiv:2302.13971, 2023.
                 [35]  Chowdhery A, Narang S, Devlin J, et al. PaLM: Scaling language modeling with pathways. arXiv:2204.02311, 2022.
                 [36]  Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. In: Proc. of the
                      31st Int’l Conf. on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017. 6000–6010.
                 [37]  Hoffmann J, Borgeaud S, Mensch A, et al. Training compute-optimal large language models. arXiv:2203.15556, 2022.
                 [38]  Shanahan M. Talking about large language models. arXiv:2212.03551, 2023.
                 [39]  Chen M, Tworek J, Jun H, et al. Evaluating large language models trained on code. arXiv:2107.03374, 2021.
                 [40]  OpenAI. Introducing ChatGPT. 2023. https://openai.com/blog/chatgpt
                 [41]  Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou YQ, Li W, Liu PJ. Exploring the limits of transfer learning with a
                      unified text-to-text Transformer. The Journal of Machine Learning Research, 2020, 21(1): 5485–5551.
                 [42]  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
                      Association for Computational Linguistics. Association for Computational Linguistics, 2020. 7871–7880. [doi: 10.18653/v1/2020.acl-
                      main.703]
                 [43]  Wang WH, Zhang YQ, Sui Y, Wan Y, Zhao Z, Wu J, Yu PS, Xu GD. Reinforcement-learning-guided source code summarization using
                      hierarchical attention. IEEE Trans. on Software Engineering, 2022, 48(1): 102–119. [doi: 10.1109/TSE.2020.2979701]
                 [44]  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]
                 [45]  Huang K, Meng XY, Zhang J, Liu Y, Wang WJ, Li SH, Zhang YQ. An empirical study on fine-tuning large language models of code for
                      automated program repair. In: Proc. of the 38th IEEE/ACM Int’l Conf. on Automated Software Engineering (ASE). Luxembourg: IEEE,
                      2023. 1162–1174. [doi: 10.1109/ASE56229.2023.00181]
                 [46]  Nashid N, Sintaha M, Mesbah A. Retrieval-based prompt selection for code-related few-shot learning. In: Proc. of the 45th IEEE/ACM
                      Int’l Conf. on Software Engineering. Melbourne: IEEE, 2023. 2450–2462. [doi: 10.1109/ICSE48619.2023.00205]
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