Page 199 - 《软件学报》2026年第7期
P. 199

2884                                                       软件学报  2026  年第  37  卷第  7  期


                 [15]   Liu JW, Xia CS, Wang YY, Zhang LM. Is your code generated by ChatGPT really correct? Rigorous evaluation of large language models
                     for code generation. In: Proc. of the 37th Int’l Conf. on Neural Information Processing Systems. New Orleans: Curran Associates Inc.,
                     2023. 943.
                 [16]   King JC. Symbolic execution and program testing. Communications of the ACM, 1976, 19(7): 385–394. [doi: 10.1145/360248.360252]
                 [17]   Cadar C, Dunbar D, Engler D. KLEE: Unassisted and automatic generation of high-coverage tests for complex systems programs. In:
                     Proc. of the 8th USENIX Conf. on Operating Systems Design and Implementation. San Diego: USENIX Association, 2008. 209–224.
                 [18]   Cadar C, Sen K. Symbolic execution for software testing: Three decades later. Communications of the ACM, 2013, 56(2): 82–90. [doi: 10.
                     1145/2408776.2408795]
                 [19]   Cadar C, Nowack M. KLEE symbolic execution engine in 2019. Int’l Journal on Software Tools for Technology Transfer, 2021, 23(6):
                     867–870. [doi: 10.1007/s10009-020-00570-3]
                 [20]   Iyer  S,  Konstas  I,  Cheung  A,  Zettlemoyer  L.  Mapping  language  to  code  in  programmatic  context.  In:  Proc.  of  the  2018  Conf.  on
                     Empirical Methods in Natural Language Processing. Brussels: ACL, 2018. 1643–1652. [doi: 10.18653/v1/D18-1192]
                 [21]   Li YJ, Choi D, Chung J, et al. Competition-level code generation with AlphaCode. Science, 2022, 378(6624): 1092–1097. [doi: 10.1126/
                     science.abq1158]
                 [22]   Zhao WX, Zhou K, Li JY, Tang TY, Wang XL, Hou YP, Min YQ, Zhang BC, Zhang JJ, Dong ZC, Du YF, Yang C, Chen YS, Chen ZP,
                     Jiang JH, Ren RY, Li YF, Tang XY, Liu ZK, Liu PY, Nie JY, Wen JR. A survey of large language models. arXiv:2303.18223, 2023.
                 [23]   OpenAI, Achiam J, Adler S, et al. GPT-4 technical report. arXiv:2303.08774, 2023.
                 [24]   Anthropic. The Claude 3 model family: Opus, Sonnet, Haiku. 2024. https://www-cdn.anthropic.com/de8ba9b01c9ab7cbabf5c33b80b7bbc
                     618857627/Model_Card_Claude_3.pdf
                 [25]   Rozière B, Gehring J, Gloeckle F, et al. Code Llama: Open foundation models for code. arXiv:2308.12950, 2023.
                 [26]   Team G, Anil R, Borgeaud S, et al. Gemini: A family of highly capable multimodal models. arXiv:2312.11805, 2023.
                 [27]   Team  G,  Georgiev  P,  Lei  VI,  et  al.  Gemini  1.5:  Unlocking  multimodal  understanding  across  millions  of  tokens  of  context.  arXiv:
                     2403.05530, 2024.
                 [28]   DeepSeek-AI,  Zhu  QH,  Guo  DY,  et  al.  DeepSeek-Coder-V2:  Breaking  the  barrier  of  closed-source  models  in  code  intelligence.
                     arXiv:2406.11931, 2024.
                 [29]   Du  ZX,  Qian  YJ,  Liu  X,  Ding  M,  Qiu  JZ,  Yang  ZL,  Tang  J.  GLM:  General  language  model  pretraining  with  autoregressive  blank
                     infilling. In: Proc. of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin: ACL, 2022. 320–335. [doi: 10.
                     18653/v1/2022.acl-long.26]
                 [30]   Zeng AH, Liu X, Du ZX, Wang ZH, Lai HY, Ding M, Yang ZY, Xu YF, Zheng WD, Xia X, Tam W, Ma ZX, Xue YF, Zhai JD, Chen
                     WG, Liu ZY, Zhang P, Dong YX, Tang J. GLM-130B: An open bilingual pre-trained model. arXiv:2210.02414, 2022.
                 [31]   Yu T, Zhang R, Yang K, Yasunaga M, Wang DX, Li ZF, Ma J, Li I, Yao QN, Roman S, Zhang ZL, Radev D. Spider: A large-scale
                     human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task. In: Proc. of the 2018 Conf. on Empirical
                     Methods in Natural Language Processing. Brussels: ACL, 2018. 3911–3921. [doi: 10.18653/v1/D18-1425]
                 [32]   Cassano  F,  Gouwar  J,  Nguyen  D,  Nguyen  S,  Phipps-Costin  L,  Pinckney  D,  Yee  MH,  Zi  YT,  Anderson  CJ,  Feldman  MQ,  Guha  A,
                     Greenberg M, Jangda A. MultiPL-E: A scalable and polyglot approach to benchmarking neural code generation. IEEE Trans. on Software
                     Engineering, 2023, 49(7): 3675–3691. [doi: 10.1109/TSE.2023.3267446]
                 [33]   Miller BP, Fredriksen L, So B. An empirical study of the reliability of UNIX utilities. Communications of the ACM, 1990, 33(12): 32–44.
                     [doi: 10.1145/96267.96279]
                 [34]   Holler C, Herzig K, Zeller A. Fuzzing with code fragments. In: Proc. of the 21st USENIX Conf. on Security Symp. Bellevue: USENIX
                     Association, 2012. 38.
                 [35]   Cadar C, Godefroid P, Khurshid S, Păsăreanu CS, Sen K, Tillmann N, Visser W. Symbolic execution for software testing in practice:
                     Preliminary assessment. In: Proc. of the 33rd Int’l Conf. on Software Engineering. Honolulu: ACM, 2011. 1066–1071. [doi: 10.1145/
                     1985793.1985995]
                 [36]   Bailey J, Nicholas C. Symbolic execution in practice: A survey of applications in vulnerability, malware, firmware, and protocol analysis.
                     arXiv:2508.06643, 2025.
                 [37]   Vouvoutsis V, Casino F, Patsakis C. Beyond the sandbox: Leveraging symbolic execution for evasive malware classification. Computers
                     & Security, 2025, 149: 104193. [doi: 10.1016/j.cose.2024.104193]
                 [38]   Chen  JC,  Shao  ZZ,  Yang  S,  Shen  YM,  Wang  YL,  Chen  T,  Shan  ZY,  Zheng  ZB.  NumScout:  Unveiling  numerical  defects  in  smart
                     contracts using LLM-pruning symbolic execution. IEEE Trans. on Software Engineering, 2025, 51(5): 1538–1553. [doi: 10.1109/TSE.
                     2025.3555622]
   194   195   196   197   198   199   200   201   202   203   204