Page 281 - 《软件学报》2024年第4期

王尚文 等: 基于指针神经网络的细粒度缺陷定位 1859

Zurich: IEEE, 2012. 837–847. [doi: 10.1109/ICSE.2012.6227135]
[58] Brody S, Alon U, Yahav E. A structural model for contextual code changes. Proc. of the ACM on Programming Languages, 2020, 4:
1–28. [doi: 10.1145/3428283]
[59] Li J, Sun AX, Han JL, Li CL. A survey on deep learning for named entity recognition. IEEE Trans. on Knowledge and Data Engineering,
2022, 34(1): 50–70. [doi: 10.1109/tkde.2020.2981314]
[60] Mannor S, Peleg D, Rubinstein R. The cross entropy method for classification. In: Proc. of the 22nd Int’l Conf. on Machine Learning.
Bonn: ACM, 2005. 561–568. [doi: 10.1145/1102351.1102422]
[61] Karampatsis RM, Sutton CA. How often do single-statement bugs occur?: The ManySStuBs4J dataset. In: Proc. of the 17th Int’l Conf. on
Mining Software Repositories. Seoul: ACM, 2020. 573–577. [doi: 10.1145/3379597.3387491]
[62] Li X, Li W, Zhang YQ, Zhang LM. DeepFL: Integrating multiple fault diagnosis dimensions for deep fault localization. In: Proc. of the
28th ACM SIGSOFT Int’l Symp. on Software Testing and Analysis. Beijing: ACM, 2019. 169–180. [doi: 10.1145/3293882.3330574]
[63] Zhang MS, Li X, Zhang LM, Khurshid S. Boosting spectrum-based fault localization using PageRank. In: Proc. of the 26th ACM
SIGSOFT Int’l Symp. on Software Testing and Analysis. Santa Barbara: ACM, 2017. 261–272. [doi: 10.1145/3092703.3092731]
[64] Liu K, Kim D, Koyuncu A, Li L, Bissyandé TF, Le Traon Y. A closer look at real-world patches. In: Proc. of the 2018 IEEE Int’l Conf.
on Software Maintenance and Evolution. Madrid: IEEE, 2018. 275–286. [doi: 10.1109/ICSME.2018.00037]
[65] Arcuri A, Briand L. A practical guide for using statistical tests to assess randomized algorithms in software engineering. In: Proc. of the
33rd Int’l Conf. on Software Engineering. Honolulu: ACM, 2011. 1–10. [doi: 10.1145/1985793.1985795]
[66] Madeiral F, Urli S, Maia M, Monperrus M. BEARS: An extensible Java bug benchmark for automatic program repair studies. In: Proc. of
the 26th IEEE Int’l Conf. on Software Analysis, Evolution and Reengineering. Hangzhou: IEEE, 2019. 468–478. [doi: 10.1109/SANER.
2019.8667991]
[67] Lin D, Koppel J, Chen AGL, Solar-Lezama A. QuixBugs: A multi-lingual program repair benchmark set based on the quixey challenge.
In: Proc. of the 2017 Companion of the ACM SIGPLAN Int’l Conf. on Systems, Programming, Languages, and Applications: Software
for Humanity. Vancouver: ACM, 2017. 55–56. [doi: 10.1145/3135932.3135941]
[68] Saha R, Lyu YJ, Lam W, Yoshida H, Prasad MR. Bugs.jar: A large-scale, diverse dataset of real-world Java bugs. In: Proc. of the 15th
IEEE/ACM Int’l Conf. on Mining Software Repositories. Gothenburg: ACM, 2018. 10–13.
[69] Wang SW, Wen M, Chen LQ, Yi X, Mao XG. How different is it between machine-generated and developer-provided patches? An
empirical study on the correct patches generated by automated program repair techniques. In: Proc. of the 2019 ACM/IEEE Int’l Symp.
on Empirical Software Engineering and Measurement. Porto de Galinhas: IEEE, 2019. 1–12. [doi: 10.1109/ESEM.2019.8870172]
[70] Chen LS, Pei Y, Furia CA. Contract-based program repair without the contracts. In: Proc. of the 32nd IEEE/ACM Int ’l Conf. on
Automated Software Engineering. Urbana: IEEE, 2017. 637–647. [doi: 10.1109/ASE.2017.8115674]
[71] Koyuncu A, Liu K, Bissyandé TF, Kim D, Klein J, Monperrus M, Le Traon Y. FixMiner: Mining relevant fix patterns for automated
program repair. Empirical Software Engineering, 2020, 25(3): 1980–2024. [doi: 10.1007/s10664-019-09780-z]
[72] Li Y, Wang SH, Nguyen TN. DLFix: Context-based code transformation learning for automated program repair. In: Proc. of the 42nd
IEEE/ACM Int’l Conf. on Software Engineering. Seoul: ACM, 2020. 602–614.
[73] Durieux T, Monperrus M. DynaMoth: Dynamic code synthesis for automatic program repair. In: Proc. of the 11th Int’l Workshop on
Automation of Software Test. Austin: ACM, 2016. 85–91. [doi: 10.1145/2896921.2896931]
[74] Xuan JF, Martinez M, Demarco F, Clément M, Marcote SL, Durieux T, Berre DL, Monperrus M. Nopol: Automatic repair of conditional
statement bugs in java programs. IEEE Trans. on Software Engineering, 2017, 43(1): 34–55. [doi: 10.1109/TSE.2016.2560811]
[75] Yuan Y, Banzhaf W. ARJA: Automated repair of Java programs via multi-objective genetic programming. IEEE Trans. on Software
Engineering, 2020, 46(10): 1040–1067. [doi: 10.1109/TSE.2018.2874648]
[76] Wang SW, Mao XG, Niu N, Yi X, Guo AB. Multi-location program repair strategies learned from successful experience. In: Proc. of the
31st Int’l Conf. on Software Engineering and Knowledge Engineering. Lisbon: KSI, 2019. 713–777. [doi: 10.18293/SEKE2019-007]