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The widespread adoption of code language models in software engineering tasks has exposed vulnerabilities to adversarial attacks, especially the identifier substitution attacks.
1909
Earlier work this paper cites.
Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of State Calculations by Fast Computing Machines. The Journal of Chemical Physics 21
1953
Earlier work this paper cites.
Ruxton, G.D.: The unequal variance t-test is an underused alternative to Student’s t-test and the Mann–Whitney U test. Behavioral Ecology 17
2006
Earlier work this paper cites.
Zhang, H.: Exploring regularity in source code: Software science and zipf’s law. In: 2008 15th Working Conference on Reverse Engineering. pp. 101–110 (2008). https://doi.org/10.1109/WCRE.2008.37
2008
Earlier work this paper cites.
2009
Earlier work this paper cites.
Hindle, A., Barr, E.T., Su, Z., Gabel, M., Devanbu, P.: On the naturalness of software. In: 2012 34th International Conference on Software Engineering (ICSE). pp. 837–847 (2012). https://doi.org/10.1109/ICSE.2012.6227135
2012
Earlier work this paper cites.
Kumar, A., Vembu, S., Menon, A.K., Elkan, C.: Beam search algorithms for multilabel learning. Mach. Learn. 92
2013
Earlier work this paper cites.
Svajlenko, J., Islam, J.F., Keivanloo, I., Roy, C.K., Mia, M.M.: Towards a big data curated benchmark of inter-project code clones. In: 2014 IEEE International Conference on Software Maintenance and Evolution. pp. 476–480 (2014). https://doi.org/10.1109/ICSME.2014.77
2014
Earlier work this paper cites.
FindBugs Project: Findbugs - find bugs in java programs (2015), https://findbugs.sourceforge.net/
2015
Earlier work this paper cites.
Iyer, S., Konstas, I., Cheung, A., Zettlemoyer, L.: Summarizing source code using a neural attention model. In: Erk, K., Smith, N.A. (eds.) Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 2073–2083. Association for Computational Linguistics, Berlin, Germany (Aug 2016). https://doi.org/10.18653/v1/P16-1195, https://aclanthology.org/P16-1195
2016
Earlier work this paper cites.
Mou, L., Li, G., Zhang, L., Wang, T., Jin, Z.: Convolutional neural networks over tree structures for programming language processing. Proceedings of the AAAI Conference on Artificial Intelligence 30
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
Jimenez, M., Checkam, T.T., Cordy, M., Papadakis, M., Kintis, M., Traon, Y.L., Harman, M.: Are mutants really natural? a study on how "naturalness" helps mutant selection. In: Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. ESEM ’18, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3239235.3240500, https://doi.org/10.1145/3239235.3240500
2018
Earlier work this paper cites.
Jimenez, M., Checkam, T.T., Cordy, M., Papadakis, M., Kintis, M., Traon, Y.L., Harman, M.: Are mutants really natural? a study on how "naturalness" helps mutant selection. In: Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. ESEM ’18, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3239235.3240500, https://doi.org/10.1145/3239235.3240500
2018
Earlier work this paper cites.
Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., Zhou, M.: CodeBERT: A pre-trained model for programming and natural languages. In: Cohn, T., He, Y., Liu, Y. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2020. pp. 1536–1547. Association for Computational Linguistics, Online (Nov 2020). https://doi.org/10.18653/v1/2020.findings-emnlp.139, https://aclanthology.org/2020.findings-emnlp.139
2020
Earlier work this paper cites.
Svyatkovskiy, A., Deng, S.K., Fu, S., Sundaresan, N.: Intellicode compose: code generation using transformer. In: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. p. 1433–1443. ESEC/FSE 2020, Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3368089.3417058, https://doi.org/10.1145/3368089.3417058
2020
Earlier work this paper cites.
Hough, K., Welearegai, G., Hammer, C., Bell, J.: Revealing injection vulnerabilities by leveraging existing tests. In: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering. p. 284–296. ICSE ’20, Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3377811.3380326, https://doi.org/10.1145/3377811.3380326
2020
Earlier work this paper cites.
Zhang, H., Li, Z., Li, G., Ma, L., Liu, Y., Jin, Z.: Generating adversarial examples for holding robustness of source code processing models. Proceedings of the AAAI Conference on Artificial Intelligence 34
2020
Earlier work this paper cites.
Yefet, N., Alon, U., Yahav, E.: Adversarial examples for models of code. Proc. ACM Program. Lang. 4
2020
Cited alongside, same era.
Casalnuovo, C., Barr, E.T., Dash, S.K., Devanbu, P., Morgan, E.: A theory of dual channel constraints. In: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: New Ideas and Emerging Results. p. 25–28. ICSE-NIER ’20, Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3377816.3381720, https://doi.org/10.1145/3377816.3381720
2020
Cited alongside, same era.
Morris, J., Lifland, E., Yoo, J.Y., Grigsby, J., Jin, D., Qi, Y.: TextAttack: A framework for adversarial attacks, data augmentation, and adversarial training in NLP. In: Liu, Q., Schlangen, D. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. pp. 119–126. Association for Computational Linguistics, Online (Oct 2020). https://doi.org/10.18653/v1/2020.emnlp-demos.16, https://aclanthology.org/2020.emnlp-demos.16
2020
Cited alongside, same era.
Zhang, J., Ma, W., Hu, Q., Liu, S., Xie, X., Le Traon, Y., Liu, Y.: A black-box attack on code models via representation nearest neighbor search. In: Bouamor, H., Pino, J., Bali, K. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2023. pp. 9706–9716. Association for Computational Linguistics, Singapore (Dec 2023). https://doi.org/10.18653/v1/2023.findings-emnlp.649, https://aclanthology.org/2023.findings-emnlp.649
2023
Later among the works it cites.
Liu, Y., Iter, D., Xu, Y., Wang, S., Xu, R., Zhu, C.: G-eval: NLG evaluation using gpt-4 with better human alignment. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. pp. 2511–2522. Association for Computational Linguistics, Singapore (Dec 2023). https://doi.org/10.18653/v1/2023.emnlp-main.153, https://aclanthology.org/2023.emnlp-main.153
2023
Later among the works it cites.
2023
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Ahmad, W., Chakraborty, S., Ray, B., Chang, K.W.: Unified pre-training for program understanding and generation. In: Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y. (eds.) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 2655–2668. Association for Computational Linguistics, Online (Jun 2021). https://doi.org/10.18653/v1/2021.naacl-main.211, https://aclanthology.org/2021.naacl-main.211
2021
Cited alongside, same era.
Lu, S., Guo, D., Ren, S., Huang, J., Svyatkovskiy, A., Blanco, A., Clement, C., Drain, D., Jiang, D., Tang, D., Li, G., Zhou, L., Shou, L., Zhou, L., Tufano, M., GONG, M., Zhou, M., Duan, N., Sundaresan, N., Deng, S.K., Fu, S., LIU, S.: Codexglue: A machine learning benchmark dataset for code understanding and generation. In: Vanschoren, J., Yeung, S. (eds.) Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks. vol. 1 (2021), https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/c16a5320fa475530d9583c34fd356ef5-Paper-round1.pdf
2021
Cited alongside, same era.
Rabin, M.R.I., Bui, N.D., Wang, K., Yu, Y., Jiang, L., Alipour, M.A.: On the generalizability of neural program models with respect to semantic-preserving program transformations. Information and Software Technology 135
2021
Cited alongside, same era.
Wang, W., Wang, R., Wang, L., Wang, Z., Ye, A.: Towards a robust deep neural network against adversarial texts: A survey. IEEE Transactions on Knowledge and Data Engineering 35
2021
Cited alongside, same era.
Zhou, Y., Zhang, X., Shen, J., Han, T., Chen, T., Gall, H.: Adversarial robustness of deep code comment generation. ACM Trans. Softw. Eng. Methodol. 31
2022
Cited alongside, same era.
Yang, Z., Shi, J., He, J., Lo, D.: Natural attack for pre-trained models of code. In: Proceedings of the 44th International Conference on Software Engineering. p. 1482–1493. ICSE ’22, Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3510003.3510146, https://doi.org/10.1145/3510003.3510146
2022
Cited alongside, same era.
Li, Z., Chen, G.Q., Chen, C., Zou, Y., Xu, S.: Ropgen: towards robust code authorship attribution via automatic coding style transformation. In: Proceedings of the 44th International Conference on Software Engineering. p. 1906–1918. ICSE ’22, Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3510003.3510181, https://doi.org/10.1145/3510003.3510181
2022
Cited alongside, same era.
Zeng, Z., Tan, H., Zhang, H., Li, J., Zhang, Y., Zhang, L.: An extensive study on pre-trained models for program understanding and generation. In: Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis. p. 39–51. ISSTA 2022, Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3533767.3534390, https://doi.org/10.1145/3533767.3534390
2022
Cited alongside, same era.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., ichter, b., Xia, F., Chi, E., Le, Q.V., Zhou, D.: Chain-of-thought prompting elicits reasoning in large language models. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems. vol. 35, pp. 24824–24837. Curran Associates, Inc. (2022), https://proceedings.neurips.cc/paper_files/paper/2022/file/9d5609613524ecf4f15af0f7b31abca4-Paper-Conference.pdf
2022
Cited alongside, same era.
Later among the works it cites.
Zhou, S., Alon, U., Agarwal, S., Neubig, G.: CodeBERTScore: Evaluating code generation with pretrained models of code. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. pp. 13921–13937. Association for Computational Linguistics, Singapore (Dec 2023). https://doi.org/10.18653/v1/2023.emnlp-main.859, https://aclanthology.org/2023.emnlp-main.859
2023
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2024
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2024
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2024
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Yang, C., Chen, J., Jiang, J., Huang, Y.: Dependency-aware code naturalness. Proceedings of the ACM on Programming Languages 8
2024
Later among the works it cites.
Yang, G., Zhou, Y., Yang, W., Yue, T., Chen, X., Chen, T.: How important are good method names in neural code generation? a model robustness perspective. ACM Trans. Softw. Eng. Methodol. 33
2024
Later among the works it cites.
Zhou, H., Wang, Z., Wang, H., Chen, D., Mu, W., Zhang, F.: Evaluating the validity of word-level adversarial attacks with large language models. In: Ku, L.W., Martins, A., Srikumar, V. (eds.) Findings of the Association for Computational Linguistics ACL 2024. pp. 4902–4922. Association for Computational Linguistics, Bangkok, Thailand and virtual meeting (Aug 2024), https://aclanthology.org/2024.findings-acl.292
2024
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2024
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2024
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2024
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2024
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2025
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SonarSource: Sonarqube documentation (2025), https://docs.sonarsource.com/sonarqube-server/latest/
2025
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