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曹帅 等: 深度学习在基于信息检索的缺陷定位中的应用综述 1555

and the 13th European Conf. on Foundations of Software Engineering. Szeged: ACM, 2011. 322–331. [doi: 10.1145/2025113.2025157]
[74] Voorhees EM. The TREC-8 question answering track report. In: Proc. of the 2nd LREC. 2000. 77–82.
[75] Manning CD, Raghavan P, Schütze H. Introduction to Information Retrieval. Cambridge: Cambridge University Press, 2008.
[76] Kochhar PS, Xia X, Lo D, LI SP, Claims A. Practitioners’ expectations on automated fault localization. In: Proc. of the 25th Int’l Symp.
on Software Testing and Analysis. Saarbrücken: ACM, 2016. 165–176. [doi: 10.1145/2931037.2931051]
[77] Youm KC, Ahn J, Lee E. Improved bug localization based on code change histories and bug reports. Information and Software
Technology, 2017, 82: 177–192. [doi: 10.1016/j.infsof.2016.11.002]
[78] Rahman S, Rahman MM, Sakib K. A statement level bug localization technique using statement dependency graph. In: Proc. of the 12th
Int’l Conf. on Evaluation of Novel Approaches to Software Engineering. Porto: SciTePress, 2017. 171–178. [doi: 10.5220/
0006261901710178]
[79] Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504–507. [doi: 10.
1126/science.1127647]
[80] Kim D, Tao YD, Kim S, Zeller A. Where should we fix this bug? A two-phase recommendation model. IEEE Trans. on Software
Engineering, 2013, 39(11): 1597–1610. [doi: 10.1109/tse.2013.24]
[81] Perozzi B, Al-Rfou R, Skiena S. Deepwalk: Online learning of social representations. In: Proc. of the 20th ACM SIGKDD Int’l Conf. on

Knowledge Discovery and Data Mining. New York: ACM, 2014. 701–710. [doi: 10.1145/2623330.2623732]
[82] Ye X, Bunescu R, Liu C. Learning to rank relevant files for bug reports using domain knowledge. In: Proc. of the 22nd ACM SIGSOFT
Int’l Symp. on Foundations of Software Engineering. ACM, 2014. 689–699. [doi: 10.1145/2635868.2635874]
[83] 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. Madrid: IEEE, 2021. 324–335. [doi: 10.1109/ICSE43902.
2021.00040]
[84] Fejzer M, Narębski J, Przymus P, Stencel K. Tracking buggy files: New efficient adaptive bug localization algorithm. IEEE Trans. on
Software Engineering, 2022, 48(7): 2557–2569. [doi: 10.1109/tse.2021.3064447]
[85] Ye X, Bunescu R, Liu C. Mapping bug reports to relevant files: A ranking model, a fine-grained benchmark, and feature evaluation. IEEE
Trans. on Software Engineering, 2016, 42(4): 379–402. [doi: 10.1109/tse.2015.2479232]
[86] Gay G, Haiduc S, Marcus A, Menzies T. On the use of relevance feedback in IR-based concept location. In: Proc. of Int’l Conf. on
Software Maintenance. 2009. 351–360. [doi: 10.1109/icsm.2009.5306315]
[87] Turhan B, Menzies T, Bener AB, Di Stefano J. On the relative value of cross-company and within-company data for defect prediction.
Empirical Software Engineering, 2009, 14(5): 540–578. [doi: 10.1007/s10664-008-9103-7]
[88] Rao S, Kak A. Retrieval from software libraries for bug localization: A comparative study of generic and composite text models. In: Proc.
of the 8th Working Conf. on Mining Software Repositories. Honolulu: ACM, 2011. 43–52. [doi: 10.1145/1985441.1985451]
[89] Nam J, Pan SJ, Kim S. Transfer defect learning. In: Proc. of the 35th Int’l Conf. on Software Engineering. San Francisco: IEEE, 2013.
382–391. [doi: 10.1109/ICSE.2013.6606584]
[90] Ye X, Shen H, Ma X, Bunescu R, Liu C. From word embeddings to document similarities for improved information retrieval in software
engineering. In: Proc. of the 38th Int’l Conf. on Software Engineering. Austin: ACM, 2016. 404–415. [doi: 10.1145/2884781.2884862]
[91] Zhang W, Li ZQ, Wang Q, Li J. FineLocator: A novel approach to method-level fine-grained bug localization by query expansion.
Information and Software Technology, 2019, 110: 121–135. [doi: 10.1016/j.infsof.2019.03.001]
[92] Rahman MM, Roy CK. Improving IR-based bug localization with context-aware query reformulation. In: Proc. of the 26th ACM Joint
Meeting on European Software Engineering Conf. and Symp. on the Foundations of Software Engineering. Lake Buena: ACM, 2018.
621–632. [doi: 10.1145/3236024.3236065]
[93] Donahue J, Anne Hendricks L, Guadarrama S, Rohrbach M, Venugopalan S, Darrell T. Long-term recurrent convolutional networks for
visual recognition and description. In: Proc. of the 2015 IEEE Conf. on Computer Vision and Pattern Recognition. Boston: IEEE, 2015.
2625–2634. [doi: 10.1109/CVPR.2015.7298878]
[94] Just R, Jalali D, Ernst MD. Defects4J: A database of existing faults to enable controlled testing studies for Java programs. In: Proc. of the
2014 Int’l Symp. on Software Testing and Analysis. San Jose: ACM, 2014. 437–440. [doi: 10.1145/2610384.2628055]
[95] Ding YRB, Chakraborty S, Buratti L, Pujar S, Morari A, Kaiser G, Ray B. CONCORD: Clone-aware contrastive learning for source code.
In: Proc. of the 32nd ACM SIGSOFT Int’l Symp. on Software Testing and Analysis. Seattle: ACM, 2023. 26–38. [doi: 10.1145/3597926.
3598035]
[96] Zeng C, Yu Y, Li SS, Xia X, Wang ZM, Geng MY, Bai LX, Dong W, Liao XK. DEGRAPHCS: Embedding variable-based flow graph
for neural code search. ACM Trans. on Software Engineering and Methodology, 2023, 32(2): 34. [doi: 10.1145/3546066]