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姜佳君  等:软件缺陷自动修复技术综述                                                              2689


         [91]    Liu K, Koyuncu A, Kim D, Bissyandé TF. Avatar: Fixing semantic bugs with fix patterns of static analysis violations. In: Proc. of
             the 2019 IEEE 26th Int’l Conf. on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2019. 1−12. [doi: 10.1109/
             SANER.2019.8667970]
         [92]    Yue RR, Meng N, Wang QX. A characterization study of repeated bug fixes. In: Proc. of the 2017 IEEE Int’l Conf. on Software
             Maintenance and Evolution (ICSME). IEEE, 2017. 422−432. [doi: 10.1109/ICSME.2017.16]
         [93]    Jiang JJ, Ren LY, Xiong YF, Zhang LM. Inferring program transformations from singular examples via big code. In: Proc. of the
             34th Int’l Conf. on Automated Software Engineering (ASE). IEEE, 2019. 255−266. [doi: 10.1109/ASE.2019.00033]
         [94]    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 (ICSE). IEEE, 2018. 60−70. [doi: 10.1145/3180155.3180219]
         [95]    Tufano M, Watson C, Bavota G, Penta MD, White M, Poshyvanyk D. An empirical study on learning bug-fixing patches in the
             wild via neural machine translation. ACM Trans. on Software Engineering and Methodology, 2019,28(4):1−29.
         [96]    See  A,  Liu PJ,  Manning  CD. Get to the point: Summarization  with pointer-generator networks.  In: Proc. of the 55th  Annual
             Meering of the Association for Computational Lunguistics (ACL). ACM, 2017. 1073−1083. [doi: 10.18653/v1/P17-1099]
         [97]    Motwani M, Sankaranarayanan S, Just  R,  Brun Y.  Do  automated program  repair techniques repair hard  and important bugs?
             Empirical Software Engineering, 2018,23(5):2901−2947.
         [98]    Yang DH, Qi YH, Mao XG. Evaluating the strategies of statement selection in automated program repair. In: Proc. of the Int’l Conf.
             on Software Analysis, Testing, and Evolution (SATE). Springer-Verlag, 2018. 33−48.
         [99]    Liu K, Koyuncu A, Bissyandé TF, Kim D, Klein J, Le Traon Y. You cannot fix what you cannot find! An investigation of fault
             localization bias in benchmarking automated program repair systems. In: Proc. of the 2019 12th IEEE Conf. on Software Testing,
             Validation and Verification (ICST). IEEE, 2019. 102−113. [doi: 10.1109/ICST.2019.00020]
        [100]    Motwani M, Soto M, Brun Y, Just R, Le Goues C. Quality of automated program repair on real-world defects. IEEE Trans. on
             Software Engineering, 2020. [doi: 10.1109/TSE.2020.2998785]
        [101]    Le XBD, Thung F, Lo D, Le Goues C. Overfitting in semantics-based automated program repair. Empirical Software Engineering,
             2018,23(5):3007−3033.
        [102]    Jiang JJ, Xiong YF, Xia X. A manual inspection of defects4j bugs and its implications for automatic program repair. Science China
             Information Sciences, 2019,62(10):200102. [doi: 10.1007/s11432-018-1465-6]
        [103]    Lou YL, Ghanbari A, Li X, Zhang LM, Zhang HT, Hao D, Zhang L. Can automated program repair refine fault localization? A
             unified debugging approach. In: Proc. of the 29th ACM SIGSOFT Int’l Symp. on Software Testing and Analysis (ISSTA). 2020.
             75−87. [doi: 10.1145/3406889]
        [104]    Noda K, Nemoto Y, Hotta K, Tanida H, Kikuchi S. Experience report: How effective is automated program repair for industrial
             software? In: Proc. of the 2020 IEEE 27th Int’l Conf. on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2020.
             612−616. [doi: 10.1109/SANER48275.2020.9054829]
        [105]    Yi  J, Tan  SH, Mechtaev  S, Bohme  M, Roychoudhury  A. A correlation study  between automated  program repair and  test-suite
             metrics. Empirical Software Engineering, 2018,23(5):2948−2979. [doi: 10.1007/s10664-017-9552-y]
        [106]    Gao X,  Mechtaev S,  Roychoudhury  A.  Crash-avoiding program  repair.  In: Proc. of the 28th  ACM SIGSOFT Int’l Symp. on
             Software Testing and Analysis (ISSTA). ACM, 2019. 8−18. [doi: 10.1145/3293882.3330558]
        [107]    Weimer W, Fry ZP, Forrest S. Leveraging program equivalence for adaptive program repair: Models and first results. In: Proc. of
             the 28th Int’l Conf. on Automated Software Engineering (ASE). IEEE, 2013. 356−366. [doi: 10.1109/ASE.2013.6693094]
        [108]    Le Goues C, Holtschulte N, Smith EK, Brun Y, Devanbu P, Forrest S, Weimer W. The ManyBugs and IntroClass benchmarks for
             automated repair of C programs. IEEE Trans. on Software Engineering, 2015,41(12):1236−1256.
        [109]    Durieux T, Monperrus M. IntroClassJava: A benchmark of 297 small and buggy Java programs. [Research Report] hal-01272126.
             Universite Lille 1. 2016. https://hal.archives-ouvertes.fr/hal-01272126
        [110]    Tan  SH, Yi  J, Mechtaev  S,  Roychoudhury A. Codeflaws:  A  programming competition benchmark  for evaluating automated
             program repair tools. In: Proc. of the 39th Int’l Conf. on Software Engineering Companion (ICSE-C). IEEE, 2017. 180−182. [doi:
             10.1109/ICSE-C.2017.76]
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