Page 120 - 《软件学报》2025年第4期
P. 120

1526                                                       软件学报  2025  年第  36  卷第  4  期


                 [121]  Csallner C, Tillmann N, Smaragdakis Y. DySy: Dynamic symbolic execution for invariant inference. In: Proc. of the 30th Int’l Conf. on
                      Software Engineering. Leipzig: ACM, 2008. 281–290. [doi: 10.1145/1368088.1368127]
                 [122]  Molina  F,  Ponzio  P,  Aguirre  N,  Frias  M.  EvoSpex:  An  evolutionary  algorithm  for  learning  postconditions.  In:  Proc.  of  the  43rd
                      IEEE/ACM Int’l Conf. on Software Engineering (ICSE). Madrid: IEEE, 2021. 1223–1235. [doi: 10.1109/ICSE43902.2021.00112]
                 [123]  Palomba F, Di Nucci D, Panichella A, Oliveto R, De Lucia A. On the diffusion of test smells in automatically generated test code: An
                      empirical  study.  In:  Proc.  of  the  9th  Int’l  Workshop  on  Search-based  Software  Testing.  Austin:  ACM,  2016.  5–14.  [doi:  10.1145/
                      2897010.2897016]
                 [124]  Watson C, Tufano M, Moran K, Bavota G, Poshyvanyk D. On learning meaningful assert statements for unit test cases. In: Proc. of the
                      42nd ACM/IEEE Int’l Conf. on Software Engineering. Seoul: ACM, 2020. 1398–1409. [doi: 10.1145/3377811.3380429]
                 [125]  Mastropaolo A, Scalabrino S, Cooper N, Palacio DN, Poshyvanyk D, Oliveto R, Bavota G. Studying the usage of text-to-text transfer
                      Transformer to support code-related tasks. In: Proc. of the 43rd IEEE/ACM Int’l Conf. on Software Engineering. Madrid: IEEE, 2021.
                      336–347. [doi: 10.1109/ICSE43902.2021.00041]
                 [126]  Mastropaolo A, Cooper N, Palacio DN, Scalabrino S, Poshyvanyk D, Oliveto R, Bavota G. Using transfer learning for code-related
                      tasks. IEEE Trans. on Software Engineering, 2023, 49(4): 1580–1598. [doi: 10.1109/TSE.2022.3183297]
                 [127]  Yu H, Lou YL, Sun K, Ran DZ, Xie T, Hao D, Li Y, Li G, Wang QX. Automated assertion generation via information retrieval and its
                      integration with deep learning. In: Proc. of the 44th Int’l Conf. on Software Engineering. Pittsburgh: ACM, 2022. 163–174. [doi: 10.
                      618(7964): 257–263. [doi: 10.1038/s41586-023-06004-9]
                      1145/3510003.3510149]
                 [128]  Nie PY, Banerjee R, Li JJ, Mooney RJ, Gligoric M. Learning deep semantics for test completion. In: Proc. of the 45th IEEE/ACM Int’l
                      Conf. on Software Engineering. Melbourne: IEEE, 2023. 2111–2123. [doi: 10.1109/ICSE48619.2023.00178]
                 [129]  Tufano  M,  Drain  D,  Svyatkovskiy  A,  Sundaresan  N.  Generating  accurate  assert  statements  for  unit  test  cases  using  pretrained
                      Transformers. In: Proc. of the 3rd ACM/IEEE Int’l Conf. on Automation of Software Test. Pittsburgh: ACM, 2022. 54–64. [doi: 10.
                      1145/3524481.3527220]
                 [130]  Dinella E, Ryan G, Mytkowicz T, Lahiri SK. TOGA: A neural method for test oracle generation. In: Proc. of the 44th Int’l Conf. on
                      Software Engineering. Pittsburgh: ACM, 2022. 2130–2141. [doi: 10.1145/3510003.3510141]
                 [131]  Liu  ZX,  Liu  K,  Xia  X,  Yang  XH.  Towards  more  realistic  evaluation  for  neural  test  oracle  generation.  In:  Proc.  of  the  32nd  ACM
                      SIGSOFT Int’l Symp. on Software Testing and Analysis. Seattle: ACM, 2023. 589–600. [doi: 10.1145/3597926.3598080]
                 [132]  Tufano  M,  Drain  D,  Svyatkovskiy  A,  Deng  SK,  Sundaresan  N.  Unit  test  case  generation  with  Transformers  and  focal  context.
                      aarXiv:2009.05617, 2021.
                 [133]  Panichella A, Panichella S, Fraser G, Sawant AA, Hellendoorn VJ. Revisiting test smells in automatically generated tests: Limitations,
                      pitfalls, and opportunities. In: Proc. of the 2020 IEEE Int’l Conf. on Software Maintenance and Evolution (ICSME). Adelaide: IEEE,
                      2020. 523–533. [doi: 10.1109/ICSME46990.2020.00056]
                 [134]  Lemieux  C,  Inala  JP,  Lahiri  SK,  Sen  S.  CodaMosa:  Escaping  coverage  plateaus  in  test  generation  with  pre-trained  large  language
                      models.  In:  Proc.  of  the  45th  IEEE/ACM  Int’l  Conf.  on  Software  Engineering.  Melbourne:  IEEE,  2023.  919–931.  [doi:  10.1109/
                      ICSE48619.2023.00085]
                 [135]  Schäfer  M,  Nadi  S,  Eghbali  A,  Tip  F.  An  empirical  evaluation  of  using  large  language  models  for  automated  unit  test  generation.
                      arXiv:2302.06527, 2023.
                 [136]  Xie ZK, Chen YH, Zhi C, Deng SG, Yin JW. ChatUniTest: A ChatGPT-based automated unit test generation tool. arXiv:2305.04764,
                      2024.
                 [137]  Chen B, Zhang FJ, Nguyen A, Zan DG, Lin ZQ, Lou JG, Chen WZ. CodeT: Code generation with generated tests. arXiv:2207.10397,
                      2022.
                 [138]  Lahiri SK, Fakhoury S, Naik A, Sakkas G, Chakraborty S, Musuvathi M, Choudhury P, Von Veh C, Inala JP, Wang CL, Gao JF.
                      Interactive code generation via test-driven user-intent formalization. arXiv:2208.05950, 2023.
                 [139]  Mankowitz  DJ,  Michi  A,  Zhernov  A,  et  al.  Faster  sorting  algorithms  discovered  using  deep  reinforcement  learning.  Nature,  2023,

                 [140]  GPT-4 “discovered” the same sorting algorithm as alphadev by removing “mov s p” | hacker news, 2024. https://news.ycombinator.com/
                      item?id=36247549
                 [141]  The  New  York  Times.  A  smarter  APP  is  watching  your  wallet.  2023.  https://www.nytimes.com/2021/03/09/business/apps-personal-
                      finance-budget.html
                 [142]  Webster RW, Hess D. A real-time software controller for a digital model railroad system. In: Proc. of the 1993 IEEE Workshop on Real-
                      time Applications. New York: IEEE, 1993. 126–130. [doi: 10.1109/RTA.1993.263102]
                 [143]  Brown D. Hospitals turn to artificial intelligence to help with an age-old problem: Doctors’ poor bedside manners. Washington Post,
   115   116   117   118   119   120   121   122   123   124   125