Page 61 - 《软件学报》2021年第9期
P. 61
姜佳君 等:软件缺陷自动修复技术综述 2685
[7] Bader J, Scott A, Pradel M, Chandra S. Getafix: Learning to fix bugs automatically. In: Proc. of the ACM Programming Language
(OOPSLA). ACM, 2019. 1−27. [doi: 10.1145/3360585]
[8] Gupta R, Pal S, Kanade A, Shevade S. Deepfix: Fixing common C language errors by deep learning. In: Proc. of the 31st AAAI
Conf. on Artificial Intelligence (AAAI). 2017. 1345−1351.
[9] Chen ZM, Kommrusch SJ, Tufano M, Pouchet LN, Poshyvanyk D, Monperrus M. Sequencer: Sequence-to-sequence learning for
end-to-end program repair. IEEE Trans. on Software Engineering, 2019. [doi: 10.1109/TSE.2019.2940179]
[10] Vasic M, Kanade A, Maniatis P, Bieber D, Singh R. Neural program repair by jointly learning to localize and repair. In: Proc. of
the 7th Int’l Conf. on Learning Representations (ICLR). 2019. 1−12
[11] 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. ACM, 2019. 31−42. [doi: 10.1145/3293882.3330577]
[12] Abreu R, Zoeteweij P, Van Gemund AJC. On the accuracy of spectrum-based fault localization. In: Proc. of Testing: Academic and
Industrial Conf. on Practice and Research Techniques-MUTATION. IEEE, 2007. 89−98. [doi: 10.1109/TAIC.PART.2007.13]
[13] Pearson S, Campos J, Just R, Fraser G, Abreu R, Ernst MD, Pang D, Keller B. Evaluating and improving fault localization. In: Proc.
of the 39th Int’l Conf. on Software Engineering (ICSE). IEEE, 2017. 609−620. [doi: 10.1109/ICSE.2017.62]
[14] Xuan JF, Monperrus M. Learning to combine multiple ranking metrics for fault localization. In: Proc. of the Int’l Conf. on Software
Maintenance and Evolution (ICSME). IEEE, 2014. 191−200. [doi: 10.1109/ICSME.2014.41]
[15] Xie X, Chen TY, Kuo FC, Xu B. A theoretical analysis of the risk evaluation formulas for spectrum-based fault localization. ACM
Trans. on Software Engineering and Methodology, 2013,22(4):1−40. [doi: 10.1145/2522920.2522924]
[16] Bian P, Liang B, Shi WC, Huang JJ, Cai Y. NAR-Miner: Discovering negative association rules from code for bug detection. In:
Proc. of the 2018 26th ACM Joint Meeting on European Software Engineering Conf. and Symp. on the Foundations of Software
Engineering (ESEC/FSE). ACM, 2018. 411−422. [doi: 10.1145/3236024.3236032]
[17] Liang B, Bian P, Zhang Y, Shi WC, You W, Cai Y. AntMiner: Mining more bugs by reducing noise interference. In: Proc. of the
38th Int’l Conf. on Software Engineering (ICSE). IEEE, 2016. 333−344. [doi: 10.1145/2884781.2884870]
[18] Li ZM, Zhou YY. PR-Miner: Automatically extracting implicit programming rules and detecting violations in large software code.
In: Proc. of the 10th European Software Engineering Conf. Held Jointly with 13th ACM SIGSOFT Int’l Symp. on Foundations of
Software Engineering (ESEC/FSE). ACM, 2005. 306−315. [doi: 10.1145/1081706.1081755]
[19] Wang QQ, Parnin C, Orso A. Evaluating the usefulness of IR-based fault localization techniques. In: Proc. of the 2015 Int’l Symp.
on Software Testing and Analysis (ISSTA). ACM, 2015. 1−11. [doi: 10.1145/2771783.2771797]
[20] Wong WE, Gao RZ, Li YH, Abreu R, Wotawa F. A survey on software fault localization. IEEE Trans. on Software Engineering,
2016,42(8):707−740. [doi: 10.1109/TSE.2016.2521368]
[21] Qi ZC, Long F, Achour S, Rinard M. An analysis of patch plausibility and correctness for generate-and-validate patch generation
systems. In: Proc. of the 2015 Int’l Symp. on Software Testing and Analysis (ISSTA). ACM, 2015. 24−36. [doi: 10.1145/2771783.
2771791]
[22] Xiong YF, Liu XY, Zeng MH, Zhang L, Huang G. Identifying patch correctness in test-based program repair. In: Proc. of the 40th
Int’l Conf. on Software Engineering (ICSE). ACM, 2018. 789−799. [doi: 10.1145/3180155.3180182]
[23] Xin Q, Reiss SP. Identifying test-suite-overfitted patches through test case generation. In: Proc. of the 26th ACM SIGSOFT Int’l
Symp. on Software Testing and Analysis (ISSTA), Vol.17. ACM, 2017. 226−236. [doi: 10.1145/3092703.3092718]
[24] Long F, Rinard M. Automatic patch generation by learning correct code. In: Proc. of the 43rd Annual ACM SIGPLAN-SIGACT
Symp. on Principles of Programming Languages (POPL). ACM, 2016. 298−312. [doi: 10.1145/2837614.2837617]
[25] 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 Int’l Conf. on Software Engineering (ICSE). IEEE, 2017. 416−426. [doi: 10.1109/ICSE.2017.45]
[26] Forrest S, Nguyen TV, Weimer W, Le Goues C. A genetic programming approach to automated software repair. In: Proc. of the
11th Annual Conf. on Genetic and Evolutionary Computation (GECCO). ACM, 2009. 947−954.
[27] Le Goues C, Dewey-Vogt M, Forrest S, Weimer W. A systematic study of automated program repair: Fixing 55 out of 105 bugs for
$8 each. In: Proc. of the 2012 34th Int’l Conf. on Software Engineering (ICSE). IEEE, 2012. 3−13.