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刘浩 等: 基于图 Transformer 的变更集错误定位方法 2101

assurance. IEEE Trans. on Software Engineering, 2013, 39(6): 757–773. [doi: 10.1109/TSE.2012.70]
[5] Wen M, Wu RX, Cheung SC. Locus: Locating bugs from software changes. In: Proc. of the 31st IEEE/ACM Int’l Conf. on Automated
Software Engineering. Singapore: IEEE, 2016. 262–273.
[6] Ciborowska A, Damevski K. Fast changeset-based bug localization with BERT. In: Proc. of the 44th Int’l Conf. on Software Engineering.
Pittsburgh: ACM, 2022. 946–957. [doi: 10.1145/3510003.3510042]
[7] Du YL, Yu ZX. Pre-training code representation with semantic flow graph for effective bug localization. In: Proc. of the 31st ACM Joint
European Software Engineering Conf. and Symp. on the Foundations of Software Engineering. San Francisco: ACM, 2023. 579–591.
[doi: 10.1145/3611643.3616338]
[8] Deng X, Ye W, Xie R, Zhang SK. Survey of source code bug detection based on deep learning. Ruan Jian Xue Bao/Journal of Software,
2023, 34(2): 625–654 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6696.htm [doi: 10.13328/j.cnki.jos.006696]
[9] Murali V, Gross L, Qian R, Chandra S. Industry-scale IR-based bug localization: A perspective from Facebook. In: Proc. of the 43rd Int’l
Conf. on Software Engineering: Software Engineering in Practice (ICSE-SEIP). Madrid: IEEE, 2021. 188–197. [doi: 10.1109/ICSE-
SEIP52600.2021.00028]
[10] Zhou X, Xu BW, Han DG, Yang Z, He JD, Lo D. CCBERT: Self-supervised code change representation learning. In: Proc. of the 2023
IEEE Int’l Conf. on Software Maintenance and Evolution (ICSME). Bogotá: IEEE, 2023. 182–193. [doi: 10.1109/ICSME58846.2023.
00028]
[11] Lin JF, Liu YL, Zeng QK, Jiang M, Cleland-Huang J. Traceability transformed: Generating more accurate links with pre-trained BERT
models. In: Proc. of the 43rd IEEE/ACM Int’l Conf. on Software Engineering (ICSE). Madrid: IEEE, 2021. 324–335. [doi: 10.1109/
ICSE43902.2021.00040]
[12] Jung TH. CommitBERT: Commit message generation using pre-trained programming language model. arXiv:2105.14242, 2021.
[13] Nie LY, Gao CY, Zhong ZC, Lam W, Liu Y, Xu ZL. CoreGen: Contextualized code representation learning for commit message
generation. Neurocomputing, 2021, 459: 97–107. [doi: 10.1016/j.neucom.2021.05.039]
[14] Gao ZP, Xia X, Lo D, Grundy J, Zimmermann T. Automating the removal of obsolete TODO comments. In: Proc. of the 29th ACM Joint
Meeting on European Software Engineering Conf. and Symp. on the Foundations of Software Engineering. Athens: ACM, 2021.
218–229. [doi: 10.1145/3468264.3468553]
[15] Luo YK, Thost V, Shi L. Transformers over directed acyclic graphs. In: Proc. of the 37th Int’l Conf. on Neural Information Processing
Systems. New Orleans: Curran Associates Inc., 2023. 2070.
[16] Wang WH, Zhang KC, Li G, Liu SQ, Li AR, Jin Z, Liu Y. Learning program representations with a tree-structured Transformer. In: Proc.
of the 2023 IEEE Int’l Conf. on Software Analysis, Evolution and Reengineering (SANER). Taipa: IEEE, 2023. 248–259. [doi: 10.1109/
SANER56733.2023.00032]
[17] Ying CX, Cai TL, Luo SJ, Zheng SX, Ke GL, He D, Shen YM, Liu TY. Do Transformers really perform bad for graph representation? In:
Proc. of the 35th Int’l Conf. on Neural Information Processing Systems. Curran Associates Inc., 2021. 2212.
[18] Falleri JR, Morandat F, Blanc X, Martinez M, Monperrus M. Fine-grained and accurate source code differencing. In: Proc. of the 29th
ACM/IEEE Int’l Conf. on Automated Software Engineering. Vasteras: ACM, 2014. 313–324. [doi: 10.1145/2642937.2642982]
[19] Falleri JR, Martinez M. Fine-grained, accurate, and scalable source code differencing. In: Proc. of the 46th IEEE/ACM Int’l Conf. on
Software Engineering. Lisbon: IEEE, 2024. 2856–2867. [doi: 10.1145/3597503.3639148]
[20] An GB, Hong JG, Kim N, Yoo S. Fonte: Finding bug inducing commits from failures. In: Proc. of the 45th Int’l Conf. on Software
Engineering (ICSE). Melbourne: IEEE, 2023. 589–601. [doi: 10.1109/ICSE48619.2023.00059]
[21] Yin PC, Neubig G, Allamanis M, Brockschmidt M, Gaunt AL. Learning to represent edits. arXiv:1810.13337, 2018.
[22] Panthaplackel S, Li JJ, Gligoric M, Mooney RJ. Deep just-in-time inconsistency detection between comments and source code. In: Proc.
of the 35th AAAI Conf. on Artificial Intelligence. AAAI, 2021. 427–435. [doi: 10.1609/aaai.v35i1.16119]
[23] Panthaplackel S, Allamanis M, Brockschmidt M. Copy that! Editing sequences by copying spans. In Proc. of the 35th AAAI Conf. on
Artificial Intelligence. AAAI, 2021. 13622–13630. [doi: 10.1609/aaai.v35i15.17606]
[24] Cao YK, Sun ZY, Zou YZ, Xie B. Structurally-enhanced approach for automatic code change transformation. Ruan Jian Xue Bao/Journal
of Software, 2021, 32(4): 1006–1022 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6227.htm [doi: 10.13328/j.cnki.
jos.006227]
[25] Dong JH, Lou YL, Zhu QH, Sun ZY, Li ZL, Zhang WJ, Hao D. FIRA: Fine-grained graph-based code change representation for
automated commit message generation. In: Proc. of the 44th Int’l Conf. on Software Engineering. Pittsburgh: IEEE, 2022. 970–981. [doi:
10.1145/3510003.3510069]
[26] Tarjan R. Depth-first search and linear graph algorithms. In: Proc. of the 12th Annual Symp. on Switching and Automata Theory (SWAT
1971). East Lansing: IEEE, 1971. 114–121. [doi: 10.1109/SWAT.1971.10]
[27] Zhang K, Mao ZD, Wang Q, Zhang YD. Negative-aware attention framework for image-text matching. In: Proc. of the 2022 IEEE/CVF
Conf. on Computer Vision and Pattern Recognition (CVPR). New Orleans: IEEE, 2022. 15640–15649. [doi: 10.1109/CVPR52688.2022.
01521]