{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T15:26:44Z","timestamp":1782746804124,"version":"3.54.5"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62002309"],"award-info":[{"award-number":["62002309"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61872312"],"award-info":[{"award-number":["61872312"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Autom Softw Eng"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s10515-025-00560-2","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T01:39:21Z","timestamp":1758591561000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["PIONEER: improving the robustness of student models when compressing pre-trained models of code"],"prefix":"10.1007","volume":"33","author":[{"given":"Xiangyue","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinwei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lili","family":"Bo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxue","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaobing","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"560_CR1","doi-asserted-by":"crossref","unstructured":"Arcuri, A., Briand, L.: A practical guide for using statistical tests to assess randomized algorithms in software engineering. In: Proceedings of the 33rd International Conference on Software Engineering, pp. 1\u201310 (2011)","DOI":"10.1145\/1985793.1985795"},{"issue":"145","key":"560_CR2","first-page":"3","volume":"2006","author":"D Bartholomew","year":"2006","unstructured":"Bartholomew, D.: Qemu: a multihost, multitarget emulator. Linux J. 2006(145), 3 (2006)","journal-title":"Linux J."},{"issue":"8","key":"560_CR3","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798\u20131828 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"560_CR4","doi-asserted-by":"publisher","first-page":"150672","DOI":"10.1109\/ACCESS.2020.3016774","volume":"8","author":"Z Bilgin","year":"2020","unstructured":"Bilgin, Z., Ersoy, M.A., Soykan, E.U., Tomur, E., \u00c7omak, P., Kara\u00e7ay, L.: Vulnerability prediction from source code using machine learning. IEEE Access 8, 150672\u2013150684 (2020)","journal-title":"IEEE Access"},{"issue":"2","key":"560_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3624744","volume":"33","author":"S Cao","year":"2023","unstructured":"Cao, S., Sun, X., Bo, L., Wu, R., Li, B., Wu, X., Tao, C., Zhang, T., Liu, W.: Learning to detect memory-related vulnerabilities. ACM Trans. Softw. Eng. Methodology 33(2), 1\u201335 (2023)","journal-title":"ACM Trans. Softw. Eng. Methodology"},{"key":"560_CR6","unstructured":"Cheng, Y., Wang, D., Zhou, P., Zhang, T.: A survey of model compression and acceleration for deep neural networks. arXiv:1710.09282 (2017)"},{"key":"560_CR7","doi-asserted-by":"crossref","unstructured":"Cho, J.H., Hariharan, B.: On the efficacy of knowledge distillation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4794\u20134802 (2019)","DOI":"10.1109\/ICCV.2019.00489"},{"key":"560_CR8","doi-asserted-by":"crossref","unstructured":"Choi, Y., Kim, H., Lee, J.-H.: Tabs: efficient textual adversarial attack for pre-trained nl code model using semantic beam search. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 5490\u20135498 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.369"},{"issue":"1","key":"560_CR9","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1002\/sim.4780040112","volume":"4","author":"J Cuzick","year":"1985","unstructured":"Cuzick, J.: A wilcoxon-type test for trend. Stat. Med. 4(1), 87\u201390 (1985)","journal-title":"Stat. Med."},{"key":"560_CR10","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018)"},{"key":"560_CR11","doi-asserted-by":"crossref","unstructured":"Dutertre, B.: Yices 2.2. In: International Conference on Computer Aided Verification, pp. 737\u2013744. Springer (2014)","DOI":"10.1007\/978-3-319-08867-9_49"},{"key":"560_CR12","unstructured":"Fan, A., Grave, E., Joulin, A.: Reducing transformer depth on demand with structured dropout. arXiv:1909.11556 (2019)"},{"key":"560_CR13","doi-asserted-by":"crossref","unstructured":"Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., et al.: Codebert: a pre-trained model for programming and natural languages. arXiv:2002.08155 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"560_CR14","unstructured":"Fernandes, P., Allamanis, M., Brockschmidt, M.: Structured neural summarization. arXiv:1811.01824 (2018)"},{"key":"560_CR15","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/s11023-020-09548-1","volume":"30","author":"L Floridi","year":"2020","unstructured":"Floridi, L., Chiriatti, M.: Gpt-3: its nature, scope, limits, and consequences. Mind. Mach. 30, 681\u2013694 (2020)","journal-title":"Mind. Mach."},{"key":"560_CR16","doi-asserted-by":"crossref","unstructured":"Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. In: Foundations of Genetic Algorithms vol. 1, pp. 69\u201393. Elsevier, ??? (1991)","DOI":"10.1016\/B978-0-08-050684-5.50008-2"},{"key":"560_CR17","doi-asserted-by":"crossref","unstructured":"Gordon, M.A., Duh, K., Andrews, N.: Compressing bert: studying the effects of weight pruning on transfer learning. arXiv:2002.08307 (2020)","DOI":"10.18653\/v1\/2020.repl4nlp-1.18"},{"key":"560_CR18","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou, J., Yu, B., Maybank, S.J., Tao, D.: Knowledge distillation: a survey. Int. J. Comput. Vision 129, 1789\u20131819 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"560_CR19","unstructured":"Guo, D., Ren, S., Lu, S., Feng, Z., Tang, D., Liu, S., Zhou, L., Duan, N., Svyatkovskiy, A., Fu, S., et al.: Graphcodebert: pre-training code representations with data flow. arXiv:2009.08366 (2020)"},{"key":"560_CR20","unstructured":"Guo, X., Yang, J., Zhou, H., Ye, X., Li, J.: Rosearch: search for robust student architectures when distilling pre-trained language models. arXiv:2106.03613 (2021)"},{"key":"560_CR21","doi-asserted-by":"crossref","unstructured":"Harman, M., McMinn, P., De\u00a0Souza, J.T., Yoo, S.: Search based software engineering: techniques, taxonomy, tutorial. In: LASER Summer School on Software Engineering, pp. 1\u201359. Springer, ??? (2008)","DOI":"10.1007\/978-3-642-25231-0_1"},{"key":"560_CR22","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv:1503.02531 (2015)"},{"key":"560_CR23","doi-asserted-by":"crossref","unstructured":"Hu, X., Li, G., Xia, X., Lo, D., Jin, Z.: Deep code comment generation. In: Proceedings of the 26th Conference on Program Comprehension, pp. 200\u2013210 (2018)","DOI":"10.1145\/3196321.3196334"},{"key":"560_CR24","doi-asserted-by":"crossref","unstructured":"Huang, B., Chen, M., Wang, Y., Lu, J., Cheng, M., Wang, W.: Boosting accuracy and robustness of student models via adaptive adversarial distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 24668\u201324677 (2023)","DOI":"10.1109\/CVPR52729.2023.02363"},{"key":"560_CR25","doi-asserted-by":"crossref","unstructured":"Liu, W., Zhou, P., Zhao, Z., Wang, Z., Deng, H., Ju, Q.: Fastbert: a self-distilling bert with adaptive inference time. arXiv:2004.02178 (2020)","DOI":"10.18653\/v1\/2020.acl-main.537"},{"key":"560_CR26","unstructured":"Lu, S., Guo, D., Ren, S., Huang, J., Svyatkovskiy, A., Blanco, A., Clement, C., Drain, D., Jiang, D., Tang, D., et al.: Codexglue: a machine learning benchmark dataset for code understanding and generation. arXiv:2102.04664 (2021)"},{"issue":"OOPSLA","key":"560_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3360578","volume":"3","author":"S Luan","year":"2019","unstructured":"Luan, S., Yang, D., Barnaby, C., Sen, K., Chandra, S.: Aroma: code recommendation via structural code search. Proc. ACM Program. Lang. 3(OOPSLA), 1\u201328 (2019)","journal-title":"Proc. ACM Program. Lang."},{"key":"560_CR28","unstructured":"Michel, P., Levy, O., Neubig, G.: Are sixteen heads really better than one? Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"560_CR29","doi-asserted-by":"crossref","unstructured":"Mirjalili, S., Mirjalili, S.: Genetic algorithm. Evolutionary Algorithms and Neural Networks: Theory and Applications, 43\u201355 (2019)","DOI":"10.1007\/978-3-319-93025-1_4"},{"key":"560_CR30","doi-asserted-by":"crossref","unstructured":"Morris, J.X., Lifland, E., Yoo, J.Y., Grigsby, J., Jin, D., Qi, Y.: Textattack: a framework for adversarial attacks, data augmentation, and adversarial training in nlp. arXiv:2005.05909 (2020)","DOI":"10.18653\/v1\/2020.emnlp-demos.16"},{"key":"560_CR31","unstructured":"Nijkamp, E., Pang, B., Hayashi, H., Tu, L., Wang, H., Zhou, Y., Savarese, S., Xiong, C.: Codegen: an open large language model for code with multi-turn program synthesis. arXiv:2203.13474 (2022)"},{"issue":"2","key":"560_CR32","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1109\/TSE.2017.2663435","volume":"44","author":"A Panichella","year":"2017","unstructured":"Panichella, A., Kifetew, F.M., Tonella, P.: Automated test case generation as a many-objective optimisation problem with dynamic selection of the targets. IEEE Trans. Software Eng. 44(2), 122\u2013158 (2017)","journal-title":"IEEE Trans. Software Eng."},{"key":"560_CR33","doi-asserted-by":"crossref","unstructured":"Pour, M.V., Li, Z., Ma, L., Hemmati, H.: A search-based testing framework for deep neural networks of source code embedding. In: 2021 14th IEEE Conference on Software Testing, Verification and Validation (ICST), pp. 36\u201346. IEEE (2021)","DOI":"10.1109\/ICST49551.2021.00016"},{"key":"560_CR34","unstructured":"Quiring, E., Maier, A., Rieck, K.: Misleading authorship attribution of source code using adversarial learning. In: 28th USENIX Security Symposium (USENIX Security 19), pp. 479\u2013496 (2019)"},{"key":"560_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2021.106552","volume":"135","author":"MRI Rabin","year":"2021","unstructured":"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. Inf. Softw. Technol. 135, 106552 (2021)","journal-title":"Inf. Softw. Technol."},{"issue":"7","key":"560_CR36","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1016\/j.infsof.2013.01.008","volume":"55","author":"D Rattan","year":"2013","unstructured":"Rattan, D., Bhatia, R., Singh, M.: Software clone detection: a systematic review. Inf. Softw. Technol. 55(7), 1165\u20131199 (2013)","journal-title":"Inf. Softw. Technol."},{"key":"560_CR37","doi-asserted-by":"crossref","unstructured":"Raychev, V., Vechev, M., Yahav, E.: Code completion with statistical language models. In: Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 419\u2013428 (2014)","DOI":"10.1145\/2594291.2594321"},{"key":"560_CR38","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/s10515-010-0064-x","volume":"17","author":"R Robbes","year":"2010","unstructured":"Robbes, R., Lanza, M.: Improving code completion with program history. Autom. Softw. Eng. 17, 181\u2013212 (2010)","journal-title":"Autom. Softw. Eng."},{"issue":"115","key":"560_CR39","first-page":"64","volume":"541","author":"CK Roy","year":"2007","unstructured":"Roy, C.K., Cordy, J.R.: A survey on software clone detection research. Queen\u2019s School of Computing TR 541(115), 64\u201368 (2007)","journal-title":"Queen\u2019s School of Computing TR"},{"key":"560_CR40","doi-asserted-by":"crossref","unstructured":"Shi, J., Yang, Z., Kang, H.J., Xu, B., He, J., Lo, D.: Greening large language models of code. In: Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Society, pp. 142\u2013153 (2024)","DOI":"10.1145\/3639475.3640097"},{"key":"560_CR41","doi-asserted-by":"crossref","unstructured":"Shi, J., Yang, Z., Xu, B., Kang, H.J., Lo, D.: Compressing pre-trained models of code into 3 mb. In: Proceedings of the 37th IEEE\/ACM International Conference on Automated Software Engineering, pp. 1\u201312 (2022)","DOI":"10.1145\/3551349.3556964"},{"issue":"1","key":"560_CR42","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1086\/317537","volume":"32","author":"B Spellberg","year":"2001","unstructured":"Spellberg, B., Edwards Jr, J.E.: Type 1\/type 2 immunity in infectious diseases. Clin. Infect. Dis. 32(1), 76\u2013102 (2001)","journal-title":"Clin. Infect. Dis."},{"key":"560_CR43","unstructured":"Srikant, S., Liu, S., Mitrovska, T., Chang, S., Fan, Q., Zhang, G., O\u2019Reilly, U.-M.: Generating adversarial computer programs using optimized obfuscations. arXiv:2103.11882 (2021)"},{"issue":"9\u201310","key":"560_CR44","doi-asserted-by":"publisher","first-page":"5439","DOI":"10.1007\/s11042-018-5748-4","volume":"79","author":"X Sun","year":"2020","unstructured":"Sun, X., He, J.: A novel approach to generate a large scale of supervised data for short text sentiment analysis. Multimed. Tools Appl. 79(9\u201310), 5439\u20135459 (2020)","journal-title":"Multimed. Tools Appl."},{"key":"560_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/J.JSS.2023.111790","volume":"204","author":"X Sun","year":"2023","unstructured":"Sun, X., Ye, Z., Bo, L., Wu, X., Wei, Y., Zhang, T., Li, B.: Automatic software vulnerability assessment by extracting vulnerability elements. J. Syst. Softw. 204, 111790 (2023). https:\/\/doi.org\/10.1016\/J.JSS.2023.111790","journal-title":"J. Syst. Softw."},{"key":"560_CR46","doi-asserted-by":"crossref","unstructured":"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\u2013480. IEEE (2014)","DOI":"10.1109\/ICSME.2014.77"},{"key":"560_CR47","unstructured":"Tang, R., Lu, Y., Liu, L., Mou, L., Vechtomova, O., Lin, J.: Distilling task-specific knowledge from bert into simple neural networks. arXiv:1903.12136 (2019)"},{"issue":"146","key":"560_CR48","first-page":"10","volume":"2006","author":"S Tomar","year":"2006","unstructured":"Tomar, S.: Converting video formats with ffmpeg. Linux J. 2006(146), 10 (2006)","journal-title":"Linux J."},{"key":"560_CR49","doi-asserted-by":"publisher","first-page":"141987","DOI":"10.1109\/ACCESS.2019.2943639","volume":"7","author":"F Ullah","year":"2019","unstructured":"Ullah, F., Wang, J., Jabbar, S., Al-Turjman, F., Alazab, M.: Source code authorship attribution using hybrid approach of program dependence graph and deep learning model. IEEE Access 7, 141987\u2013141999 (2019)","journal-title":"IEEE Access"},{"key":"560_CR50","unstructured":"Wang, K., Christodorescu, M.: Coset: a benchmark for evaluating neural program embeddings. arXiv:1905.11445 (2019)"},{"key":"560_CR51","doi-asserted-by":"crossref","unstructured":"Wang, W., Li, G., Ma, B., Xia, X., Jin, Z.: Detecting code clones with graph neural network and flow-augmented abstract syntax tree. In: 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 261\u2013271. IEEE (2020)","DOI":"10.1109\/SANER48275.2020.9054857"},{"key":"560_CR52","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, W., Joty, S., Hoi, S.C.: Codet5: identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. arXiv:2109.00859 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.685"},{"key":"560_CR53","doi-asserted-by":"crossref","unstructured":"Wei, H., Li, M.: Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code. In: IJCAI, pp. 3034\u20133040 (2017)","DOI":"10.24963\/ijcai.2017\/423"},{"key":"560_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/J.INFSOF.2023.107178","volume":"158","author":"Y Wei","year":"2023","unstructured":"Wei, Y., Bo, L., Sun, X., Li, B., Zhang, T., Tao, C.: Automated event extraction of cve descriptions. Inf. Softw. Technol. 158, 107178 (2023). https:\/\/doi.org\/10.1016\/J.INFSOF.2023.107178","journal-title":"Inf. Softw. Technol."},{"key":"560_CR55","doi-asserted-by":"crossref","unstructured":"White, M., Tufano, M., Vendome, C., Poshyvanyk, D.: Deep learning code fragments for code clone detection. In: Proceedings of the 31st IEEE\/ACM International Conference on Automated Software Engineering, pp. 87\u201398 (2016)","DOI":"10.1145\/2970276.2970326"},{"key":"560_CR56","doi-asserted-by":"crossref","unstructured":"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, pp. 1482\u20131493 (2022)","DOI":"10.1145\/3510003.3510146"},{"issue":"4","key":"560_CR57","doi-asserted-by":"publisher","first-page":"936","DOI":"10.3390\/electronics12040936","volume":"12","author":"X Yu","year":"2023","unstructured":"Yu, X., Li, Z., Huang, X., Zhao, S.: Advulcode: generating adversarial vulnerable code against deep learning-based vulnerability detectors. Electronics 12(4), 936 (2023)","journal-title":"Electronics"},{"key":"560_CR58","doi-asserted-by":"crossref","unstructured":"Zafrir, O., Boudoukh, G., Izsak, P., Wasserblat, M.: Q8bert: quantized 8bit bert. In: 2019 Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing-NeurIPS Edition (EMC2-NIPS), pp. 36\u201339. IEEE (2019)","DOI":"10.1109\/EMC2-NIPS53020.2019.00016"},{"key":"560_CR59","doi-asserted-by":"crossref","unstructured":"Zeng, G., Qi, F., Zhou, Q., Zhang, T., Ma, Z., Hou, B., Zang, Y., Liu, Z., Sun, M.: Openattack: an open-source textual adversarial attack toolkit. arXiv:2009.09191 (2020)","DOI":"10.18653\/v1\/2021.acl-demo.43"},{"key":"560_CR60","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Li, Y., Wang, J., Liu, B., Li, D., Guo, Y., Chen, X., Liu, Y.: Remos: reducing defect inheritance in transfer learning via relevant model slicing. In: Proceedings of the 44th International Conference on Software Engineering, pp. 1856\u20131868 (2022a)","DOI":"10.1145\/3510003.3510191"},{"key":"560_CR61","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, X., Zhang, H., Sun, H., Wang, K., Liu, X.: A novel neural source code representation based on abstract syntax tree. In: 2019 IEEE\/ACM 41st International Conference on Software Engineering (ICSE), pp. 783\u2013794. IEEE (2019)","DOI":"10.1109\/ICSE.2019.00086"},{"issue":"3","key":"560_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3511887","volume":"31","author":"H Zhang","year":"2022","unstructured":"Zhang, H., Fu, Z., Li, G., Ma, L., Zhao, Z., Yang, H., Sun, Y., Liu, Y., Jin, Z.: Towards robustness of deep program processing models\u2014detection, estimation, and enhancement. ACM Trans. Softw. Eng. Methodology (TOSEM) 31(3), 1\u201340 (2022b)","journal-title":"ACM Trans. Softw. Eng. Methodology (TOSEM)"},{"key":"560_CR63","unstructured":"Zhou, Y., Liu, S., Siow, J., Du, X., Liu, Y.: Devign: effective vulnerability identification by learning comprehensive program semantics via graph neural networks. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"issue":"7","key":"560_CR64","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/S10664-022-10216-4","volume":"27","author":"Z Zhou","year":"2022","unstructured":"Zhou, Z., Bo, L., Wu, X., Sun, X., Zhang, T., Li, B., Zhang, J., Cao, S.: Spvf: security property assisted vulnerability fixing via attention-based models. Empir. Softw. Eng. 27(7), 171 (2022). https:\/\/doi.org\/10.1007\/S10664-022-10216-4","journal-title":"Empir. Softw. Eng."}],"container-title":["Automated Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10515-025-00560-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10515-025-00560-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10515-025-00560-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T11:15:58Z","timestamp":1768821358000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10515-025-00560-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,23]]},"references-count":64,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["560"],"URL":"https:\/\/doi.org\/10.1007\/s10515-025-00560-2","relation":{},"ISSN":["0928-8910","1573-7535"],"issn-type":[{"value":"0928-8910","type":"print"},{"value":"1573-7535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,23]]},"assertion":[{"value":"15 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"11"}}