Page 280 - 《软件学报》2024年第4期

1858 软件学报 2024 年第 35 卷第 4 期

[35] Kochhar PS, Xia X, Lo D, Li SP. Practitioners’ expectations on automated fault localization. In: Proc. of the 25th Int’l Symp. on Software
Testing and Analysis. Saarbrucken: ACM, 2016. 165–176. [doi: 10.1145/2931037.2931051]
[36] Liu K, Koyuncu A, Bissyandé TF, Kim D, Klein J, Traon YL. You cannot fix what you cannot find! An investigation of fault localization
bias in benchmarking automated program repair systems. In: Proc. of the 12th IEEE Conf. on Software Testing, Validation and
Verification. Xi’an: IEEE, 2019. 102–113. [doi: 10.1109/ICST.2019.00020]
[37] Jiang JJ, Xiong YF, Zhang HY, Gao Q, Chen XQ. Shaping program repair space with existing patches and similar code. In: Proc. of the
27th ACM SIGSOFT Int ’l Symp. on Software Testing and Analysis. Amsterdam: ACM, 2018. 298 –309. [doi: 10.1145/3213846.
3213871]
[38] Goues CL, Nguyen T, Forrest S, Weimer W. GenProg: A generic method for automatic software repair. IEEE Trans. on Software
Engineering, 2012, 38(1): 54–72. [doi: 10.1109/TSE.2011.104]
[39] Le XBD, Lo D, Goues CL. History driven program repair. In: Proc. of the 23rd IEEE Int’l Conf. on Software Analysis, Evolution, and
Reengineering. Osaka: IEEE, 2016. 213–224. [doi: 10.1109/SANER.2016.76]
[40] Nguyen HDT, Qi DW, Roychoudhury A, Chandra S. SemFix: Program repair via semantic analysis. In: Proc. of the 35th Int’l Conf. on
Software Engineering. San Francisco: IEEE, 2013. 772–781. [doi: 10.1109/ICSE.2013.6606623]
[41] Mechtaev S, Yi J, Roychoudhury A. Angelix: Scalable multiline program patch synthesis via symbolic analysis. In: Proc. of the 38th Int’l
Conf. on Software Engineering. Austin: ACM, 2016. 691–701. [doi: 10.1145/2884781.2884807]
[42] Liu K, Koyuncu A, Kim D, Bissyandé TF. TBar: Revisiting template-based automated program repair. In: Proc. of the 28th ACM
SIGSOFT Int’l Symp. on Software Testing and Analysis. Beijing: ACM, 2019. 31–42. [doi: 10.1145/3293882.3330577]
[43] Liu K, Koyuncu A, Kim D, Bissyandè TF. AVATAR: Fixing semantic bugs with fix patterns of static analysis violations. In: Proc. of the
IEEE 26th Int’l Conf. on Software Analysis, Evolution and Reengineering. Hangzhou: IEEE, 2019. 456–467. [doi: 10.1109/SANER.2019.
8667970]
[44] Lutellier T, Pham HV, Pang L, Li YT, Wei MS, Tan L. CoCoNuT: Combining context-aware neural translation models using ensemble
for program repair. In: Proc. of the 29th ACM SIGSOFT Int’l Symp. on Software Testing and Analysis. ACM, 2020. 101–114. [doi: 10.
1145/3395363.3397369]
[45] Jiang N, Lutellier T, Tan L. CURE: Code-aware neural machine translation for automatic program repair. In: Proc. of the 43rd
IEEE/ACM Int’l Conf. on Software Engineering. Madrid: IEEE, 2021. 1161–1173. [doi: 10.1109/ICSE43902.2021.00107]
[46] Allamanis M, Brockschmidt M, Khademi M. Learning to represent programs with graphs. In: Proc. of the 6th Int’l Conf. on Learning
Representations. Vancouver: ICLR, 2018.
[47] Bhatia S, Kohli P, Singh R. Neuro-symbolic program corrector for introductory programming assignments. In: Proc. of the 40th Int’l
Conf. on Software Engineering. Gothenburg: ACM, 2018. 60–70. [doi: 10.1145/3180155.3180219]
[48] Chakraborty S, Ding YRB, Allamanis M, Ray B. Codit: Code editing with tree-based neural models. IEEE Trans. on Software
Engineering, 2022, 48(4): 1385–1399. [doi: 10.1109/TSE.2020.3020502]
[49] Zhu QH, Sun ZY, Xiao YA, Zhang WJ, Yuan K, Xiong YF, Zhang L. A syntax-guided edit decoder for neural program repair. In: Proc.
of the 29th ACM Joint Meeting on European Software Engineering Conf. and the Symp. on the Foundations of Software Engineering.
Athens: ACM, 2021. 341–353. [doi: 10.1145/3468264.3468544]
[50] Xiong YF, Wang J, Yan RF, Zhang JC, Han S, Huang G, Zhang L. Precise condition synthesis for program repair. In: Proc. of the 39th
IEEE/ACM Int’l Conf. on Software Engineering. Buenos Aires: IEEE, 2017. 416–426. [doi: 10.1109/ICSE.2017.45]
[51] Wen M, Chen JJ, Wu RX, Hao D, Cheung SC. Context-aware patch generation for better automated program repair. In: Proc. of the 40th
Int’l Conf. on Software Engineering. Gothenburg: ACM, 2018. 1–11. [doi: 10.1145/3180155.3180233]
[52] 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]
[53] Binkley D, Davis M, Lawrie D, Morrell C. To camelcase or under_score. In: Proc. of the 17th IEEE Int’l Conf. on Program Comprehen-
sion. Vancouver: IEEE, 2009. 158–167. [doi: 10.1109/ICPC.2009.5090039]
[54] Hill E, Binkley D, Lawrie D, Pollock L, Vijay-Shanker K. An empirical study of identifier splitting techniques. Empirical Software
Engineering, 2014, 19(6): 1754–1780. [doi: 10.1007/s10664-013-9261-0]
[55] Rozovskaya A, Roth D. Grammatical error correction: Machine translation and classifiers. In: Proc. of the 54th Annual Meeting of the
Association for Computational Linguistics. Berlin: ACL, 2016. 2205–2215. [doi: 10.18653/v1/p16-1208]
[56] Wang LH, Zheng XQ. Improving grammatical error correction models with purpose-built adversarial examples. In: Proc. of the 2020
Conf. on Empirical Methods in Natural Language Processing. ACL, 2020. 2858–2869. [doi: 10.18653/v1/2020.emnlp-main.228]
[57] Hindle A, Barr ET, Su ZD, Gabel M, Devanbu P. On the naturalness of software. In: Proc. of the 34th Int’l Conf. on Software Engineering.