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1194 软件学报 2026 年第 37 卷第 3 期

classifiers. In: Proc. of the 38th IEEE/ACM Int’l Conf. on Automated Software Engineering. Luxembourg: IEEE, 2023. 27–39. [doi: 10.
1109/ASE56229.2023.00164]
[50] Wang DZ, Jia ZY, Li SS, Yu Y, Xiong Y, Dong W, Liao XK. Bridging pre-trained models and downstream tasks for source code
understanding. In: Proc. of the 44th Int’l Conf. on Software Engineering. Pittsburgh: ACM, 2022. 287–298. [doi: 10.1145/3510003.
3510062]
[51] Zhang WW, Guo SJ, Zhang HY, Sui YL, Xue YX, Xu Y. Challenging machine learning-based clone detectors via semantic-preserving
code transformations. IEEE Trans. on Software Engineering, 2023, 49(5): 3052–3070. [doi: 10.1109/TSE.2023.3240118]
[52] Yu SW, Wang T, Wang J. Data augmentation by program transformation. Journal of Systems and Software, 2022, 190: 111304. [doi: 10.
1016/j.jss.2022.111304]
[53] Gao FJ, Wang Y, Wang K. Discrete adversarial attack to models of code. Proc. of the ACM on Programming Languages, 2023,
7(PLDI): 172–195. [doi: 10.1145/3591227]
[54] Nakamura K, Ishiura N. Random testing of C compilers based on test program generation by equivalence transformation. In: Proc. of the
2016 IEEE Asia Pacific Conf. on Circuits and Systems. Jeju: IEEE, 2016. 676–679. [doi: 10.1109/APCCAS.2016.7804063]
[55] Quiring E, Maier A, Rieck K. Misleading authorship attribution of source code using adversarial learning. In: Proc. of the 28th USENIX
Conf. on Security Symp. Santa Clara: USENIX Association, 2019. 479–496.
[56] Cheers H, Lin YQ, Smith SP. Spplagiarise: A tool for generating simulated semantics-preserving plagiarism of Java source code. In:
Proc. of the 10th IEEE Int’l Conf. on Software Engineering and Service Science. Beijing: IEEE, 2019. 617–622. [doi: 10.1109/
ICSESS47205.2019.9040853]
[57] Ding YRB, Chakraborty S, Buratti L, Pujar S, Morari A, Kaiser G, Ray B. CONCORD: Clone-aware contrastive learning for source
code. In: Proc. of the 32nd ACM SIGSOFT Int’l Symp. on Software Testing and Analysis. Seattle: ACM, 2023. 26–38. [doi: 10.1145/
3597926.3598035]
[58] Sellitto G, Iannone E, Codabux Z, Lenarduzzi V, Lucia AD, Palomba F. Toward understanding the impact of refactoring on program
comprehension. In: Proc. of the 2022 IEEE Int’l Conf. on Software Analysis, Evolution and Reengineering. Honolulu: IEEE, 2022.
731–742. [doi: 10.1109/SANER53432.2022.00090]
[59] Buse RPL, Weimer WR. Learning a metric for code readability. IEEE Trans. on Software Engineering, 2010, 36(4): 546–558. [doi: 10.
1109/TSE.2009.70]
[60] Pantiuchina J, Lanza M, Bavota G. Improving code: The (Mis) perception of quality metrics. In: Proc. of the 2018 IEEE Int’l Conf. on
Software Maintenance and Evolution. Madrid: IEEE, 2018. 80–91. [doi: 10.1109/ICSME.2018.00017]
[61] Chidamber SR, Kemerer CF. A metrics suite for object oriented design. IEEE Trans. on Software Engineering, 1994, 20(6): 476–493.
[doi: 10.1109/32.295895]
[62] McCabe TJ. A complexity measure. IEEE Trans. on Software Engineering, 1976, SE-2(4): 308–320. [doi: 10.1109/TSE.1976.233837]
[63] Scalabrino S, Linares-Vásquez M, Poshyvanyk D, Oliveto R. Improving code readability models with textual features. In: Proc. of the
24th IEEE Int’l Conf. on Program Comprehension. Austin: IEEE, 2016. 1–10. [doi: 10.1109/ICPC.2016.7503707]
[64] Hough K, Welearegai G, Hammer C, Bell J. Revealing injection vulnerabilities by leveraging existing tests. In: Proc. of the 42nd
ACM/IEEE Int’l Conf. on Software Engineering. Seoul: ACM, 2020. 284–296. [doi: 10.1145/3377811.3380326]
[65] Sayar I, Bartel A, Bodden E, Le Traon Y. An in-depth study of Java deserialization remote-code execution exploits and vulnerabilities.
ACM Trans. on Software Engineering and Methodology, 2023, 32(1): 25. [doi: 10.1145/3554732]
[66] Spoto F, Burato E, Ernst MD, Ferrara P, Lovato A, Macedonio D, Spiridon C. Static identification of injection attacks in Java. ACM
Trans. on Programming Languages and Systems, 2019, 41(3): 18. [doi: 10.1145/3332371]
[67] Li R, Allal LB, Zi YT, et al. StarCoder: May the source be with you! arXiv:2305.06161, 2023.
[68] Husain H, Wu HH, Gazit T, Allamanis M, Brockschmidt M. Codesearchnet challenge: Evaluating the state of semantic code search.
arXiv:1909.09436, 2019.
[69] Wang Y, Le H, Gotmare A, Bui N, Li JN, Hoi S. CodeT5+: Open code large language models for code understanding and generation.
In: Proc. of the 2023 Conf. on Empirical Methods in Natural Language Processing. Singapore: ACL, 2023. 1069–1088. [doi: 10.18653/
v1/2023.emnlp-main.68]
[70] Rozière B, Gehring J, Gloeckle F, et al. Code Llama: Open foundation models for code. arXiv:2308.12950, 2023.
[71] Nguyen PT, di Sipio C, di Rocco J, di Penta M, di Ruscio D. Adversarial attacks to API recommender systems: Time to wake up and
smell the coffee? In: Proc. of the 36th IEEE/ACM Int’l Conf. on Automated Software Engineering. Melbourne: IEEE, 2021. 253–265.
[doi: 10.1109/ASE51524.2021.9678946]
[72] Rabin MRI, Bui NDQ, Wang K, Yu YJ, Jiang LX, Alipour MA. On the generalizability of neural program models with respect to