Page 148 - 《软件学报》2025年第4期
P. 148
1554 软件学报 2025 年第 36 卷第 4 期
[51] Yang G, Lee B. Utilizing topic-based similar commit information and CNN-LSTM algorithm for bug localization. Symmetry, 2021,
13(3): 406. [doi: 10.3390/sym13030406]
[52] 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]
[53] Zhu ZY, Tong HH, Wang Y, Li Y. BL-GAN: Semi-supervised bug localization via generative adversarial network. IEEE Trans. on
Knowledge and Data Engineering, 2023, 35(11): 11112–11125. [doi: 10.1109/tkde.2022.3225329]
[54] Liang HL, Hang DJ, Li XY. Modeling function-level interactions for file-level bug localization. Empirical Software Engineering, 2022,
27(7): 186. [doi: 10.1007/s10664-022-10237-z]
[55] Luo ZM, Wang WY, Cen CC. Improving bug localization with effective contrastive learning representation. IEEE Access, 2023, 11:
32523–32533. [doi: 10.1109/access.2022.3228802]
[56] Ma YF, Li M. Learning from the multi-level abstraction of the control flow graph via alternating propagation for bug localization. In:
Proc. of the 2022 IEEE Int’l Conf. on Data Mining. Orlando: IEEE, 2022. 299–308. [doi: 10.1109/icdm54844.2022.00040]
[57] Ma YF, Li M. The flowing nature matters: Feature learning from the control flow graph of source code for bug localization. Machine
Learning, 2022, 111(3): 853–870. [doi: 10.1007/s10994-021-06078-4]
[58] Chen H, Yang HY, Yan ZL, Kuang L, Zhang LY. CGMBL: Combining GAN and method name for bug localization. In: Proc. of the
2023, 11: 35901–35913. [doi: 10.1109/access.2023.3265731]
22nd IEEE Int’l Conf. on Software Quality, Reliability and Security. Guangzhou: IEEE, 2022. 231–241. [doi: 10.1109/qrs57517.2022.
00033]
[59] Zhu ZY, Tong HH, Wang Y, Li Y. Enhancing bug localization with bug report decomposition and code hierarchical network. Knowledge-
based Systems, 2022, 248: 108741. [doi: 10.1016/j.knosys.2022.108741]
[60] Shi XY, Ju XL, Chen X, Lu GL, Xu MQ. SemirFL: Boosting fault localization via combining semantic information and information
retrieval. In: Proc. of the 22nd IEEE Int’l Conf. on Software Quality, Reliability, and Security Companion. Guangzhou: IEEE, 2022.
324–332. [doi: 10.1109/qrs-c57518.2022.00055]
[61] Kim M, Kim Y, Lee E. An empirical study of IR-based bug localization for deep learning-based software. In: Proc. of the 2022 IEEE
Conf. on Software Testing, Verification and Validation. Valencia: IEEE, 2022. 128–139. [doi: 10.1109/icst53961.2022.00024]
[62] Huang XX, Xiang C, Li H, He P. SBuglocater: Bug localization based on deep matching and information retrieval. Mathematical
Problems in Engineering, 2022, 2022: 3987981. [doi: 10.1155/2022/3987981]
[63] Chakraborty P, Alfadel M, Nagappan M. RLocator: Reinforcement learning for bug localization. IEEE Trans. on Software Engineering,
2024, 50(10): 2695–2708. [doi: 10.1109/tse.2024.3452595]
[64] Al-Aidaroos AS, Bamzahem SM. The impact of GloVe and Word2Vec word-embedding technologies on bug localization with
convolutional neural network. Int’l Journal of Science and Engineering Applications, 2023, 12(1): 108–111. [doi: 10.7753/ijsea1201.
1035]
[65] Ciborowska A, Damevski K. Too few bug reports? Exploring data augmentation for improved changeset-based bug localization. arXiv:
2305.16430, 2023.
[66] Ahmad AA, Yu LS, Kholief M, Garba A. AttentiveBugLocator: A bug localization model using attention-based semanticfeatures and
information retrieval. 2023. [doi: 10.21203/rs.3.rs-3348519/v1]
[67] Xiao X, Xiao RJ, Li Q, Lv JH, Cui SY, Liu QX. BugRadar: Bug localization by knowledge graph link prediction. Information and
Software Technology, 2023, 162: 107274. [doi: 10.1016/j.infsof.2023.107274]
[68] Ma YF, Du YL, Li M. Capturing the long-distance dependency in the control flow graph via structural-guided attention for bug
localization. In: Proc. of the 32nd Int’l Joint Conf. on Artificial Intelligence. 2023. 2242–2250. [doi: 10.24963/ijcai.2023/249]
[69] Mohsen AM, Hassan HA, Wassif KT, Moawad R, Makady SH. Enhancing bug localization using phase-based approach. IEEE Access,
[70] 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. New York: ACM, 2023. 579–591. [doi:
10.1145/3611643.3616338]
[71] Ali W, Bo LL, Sun XB, Wu XX, Memon S, Siraj S, Suwaree Ashton A. Automated software bug localization enabled by meta-heuristic-
based convolutional neural network and improved deep neural network. Expert Systems with Applications, 2023, 232: 120562. [doi: 10.
1016/j.eswa.2023.120562]
[72] Xu GQ, Wang XQ, Wei D, Shao YL, Chen B. Bug localization with features crossing and structured semantic information matching. Int’l
Journal of Software Engineering and Knowledge Engineering, 2023, 33(8): 1261–1291. [doi: 10.1142/s0218194023500316]
[73] Rahman F, Posnett D, Hindle A, Barr E, Devanbu P. BugCache for inspections: Hit or miss? In: Proc. of the 19th ACM SIGSOFT Symp.