{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T13:02:27Z","timestamp":1761570147558,"version":"build-2065373602"},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","funder":[{"name":"National Natural Science Foundation of China","award":["62272225"],"award-info":[{"award-number":["62272225"]}]},{"name":"National Natural Science Foundation of China","award":["62302212"],"award-info":[{"award-number":["62302212"]}]},{"name":"Natural Science Foundation of Jiangsu Province under Grant","award":["BK20230877"],"award-info":[{"award-number":["BK20230877"]}]},{"name":"Foundation of the Key National Laboratory of New Technology in Computer Software (Nanjing University)","award":["KFKT2025B58"],"award-info":[{"award-number":["KFKT2025B58"]}]},{"name":"Foundation of the Postgraduate Research Practice Innovation Program (Nanjing University of Aeronautics and Astronautics)","award":["xcxjh20241603"],"award-info":[{"award-number":["xcxjh20241603"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,6,20]]},"DOI":"10.1145\/3755881.3755907","type":"proceedings-article","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T11:46:17Z","timestamp":1761565577000},"page":"487-497","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["VDLS: A Vulnerability Detection Approach Based on Execution Path Selection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1033-2449","authenticated-orcid":false,"given":"Xuanyan","family":"Zhu","sequence":"first","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8437-6640","authenticated-orcid":false,"given":"Jingxuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9618-4697","authenticated-orcid":false,"given":"Yixuan","family":"Tang","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China and State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0913-1539","authenticated-orcid":false,"given":"Weiqin","family":"Zou","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3413-4712","authenticated-orcid":false,"given":"Jiayi","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4646-1928","authenticated-orcid":false,"given":"Han","family":"Luo","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China"}]}],"member":"320","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"e_1_3_3_2_2_2","volume-title":"arstechnica","year":"2024","unstructured":"2024. arstechnica. https:\/\/arstechnica.com\/security\/2024\/03\/microsoft-says-kremlin-backed-hackers-accessed-its-source-and-internal-systems\/"},{"key":"e_1_3_3_2_3_2","volume-title":"CodeBERT","year":"2024","unstructured":"2024. CodeBERT. https:\/\/github.com\/microsoft\/CodeBERT"},{"key":"e_1_3_3_2_4_2","volume-title":"Common Weakness Enumeration: CWE","year":"2024","unstructured":"2024. Common Weakness Enumeration: CWE. https:\/\/cwe.mitre.org"},{"key":"e_1_3_3_2_5_2","volume-title":"Joern","year":"2024","unstructured":"2024. Joern. https:\/\/joern.io"},{"key":"e_1_3_3_2_6_2","volume-title":"NVD","year":"2024","unstructured":"2024. NVD. https:\/\/nvd.nist.gov\/"},{"key":"e_1_3_3_2_7_2","volume-title":"PyTorch","year":"2024","unstructured":"2024. PyTorch. https:\/\/pytorch.org"},{"key":"e_1_3_3_2_8_2","volume-title":"wiki","year":"2024","unstructured":"2024. wiki. https:\/\/en.wikipedia.org\/wiki\/2024_CrowdStrike-related_IT_outages"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"crossref","unstructured":"Sicong Cao Xiaobing Sun Lili Bo Ying Wei and Bin Li. 2021. Bgnn4vd: Constructing bidirectional graph neural-network for vulnerability detection. Information and Software Technology 136 (2021) 106576.","DOI":"10.1016\/j.infsof.2021.106576"},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510219"},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3597503.3639168"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"crossref","unstructured":"Saikat Chakraborty Rahul Krishna Yangruibo Ding and Baishakhi Ray. 2021. Deep learning based vulnerability detection: Are we there yet? IEEE Transactions on Software Engineering 48 9 (2021) 3280\u20133296.","DOI":"10.1109\/TSE.2021.3087402"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"crossref","unstructured":"Xiao Cheng Haoyu Wang Jiayi Hua Guoai Xu and Yulei Sui. 2021. Deepwukong: Statically detecting software vulnerabilities using deep graph neural network. ACM Transactions on Software Engineering and Methodology (TOSEM) 30 3 (2021) 1\u201333.","DOI":"10.1145\/3436877"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3533767.3534371"},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/1250734.1250789"},{"key":"e_1_3_3_2_16_2","unstructured":"Anton Cheshkov Pavel Zadorozhny and Rodion Levichev. 2023. Evaluation of chatgpt model for vulnerability detection. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2304.07232 (2023)."},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Yukun Dong Yeer Tang Xiaotong Cheng Yufei Yang and Shuqi Wang. 2023. SedSVD: Statement-level software vulnerability detection based on Relational Graph Convolutional Network with subgraph embedding. Information and Software Technology 158 (2023) 107168.","DOI":"10.1016\/j.infsof.2023.107168"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Melanie Ehrenberg Shahram Sarkani and Thomas\u00a0A Mazzuchi. 2024. Python source code vulnerability detection with named entity recognition. Computers & Security 140 (2024) 103802.","DOI":"10.1016\/j.cose.2024.103802"},{"key":"e_1_3_3_2_19_2","first-page":"72","volume-title":"2019 IEEE\/ACM 41st International Conference on Software Engineering (ICSE)","author":"Fan Gang","year":"2019","unstructured":"Gang Fan, Rongxin Wu, Qingkai Shi, Xiao Xiao, Jinguo Zhou, and Charles Zhang. 2019. Smoke: scalable path-sensitive memory leak detection for millions of lines of code. In 2019 IEEE\/ACM 41st International Conference on Software Engineering (ICSE). IEEE, 72\u201382."},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3379597.3387501"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN55064.2022.9892280"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/1134285.1134321"},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3524842.3527949"},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/978-3-642-54862-8_26","volume-title":"Tools and Algorithms for the Construction and Analysis of Systems: 20th International Conference, TACAS 2014, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2014, Grenoble, France, April 5-13, 2014. Proceedings 20","author":"Kroening Daniel","year":"2014","unstructured":"Daniel Kroening and Michael Tautschnig. 2014. CBMC\u2013C Bounded Model Checker: (Competition Contribution). In Tools and Algorithms for the Construction and Analysis of Systems: 20th International Conference, TACAS 2014, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2014, Grenoble, France, April 5-13, 2014. Proceedings 20. Springer, 389\u2013391."},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3468597"},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"crossref","unstructured":"Zhen Li Deqing Zou Shouhuai Xu Hai Jin Yawei Zhu and Zhaoxuan Chen. 2021. Sysevr: A framework for using deep learning to detect software vulnerabilities. IEEE Transactions on Dependable and Secure Computing 19 4 (2021) 2244\u20132258.","DOI":"10.1109\/TDSC.2021.3051525"},{"key":"e_1_3_3_2_27_2","unstructured":"Zhen Li Deqing Zou Shouhuai Xu Xinyu Ou Hai Jin Sujuan Wang Zhijun Deng and Yuyi Zhong. 2018. Vuldeepecker: A deep learning-based system for vulnerability detection. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1801.01681 (2018)."},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"crossref","unstructured":"Guanjun Lin Sheng Wen Qing-Long Han Jun Zhang and Yang Xiang. 2020. Software vulnerability detection using deep neural networks: a survey. Proc. IEEE 108 10 (2020) 1825\u20131848.","DOI":"10.1109\/JPROC.2020.2993293"},{"key":"e_1_3_3_2_29_2","doi-asserted-by":"crossref","unstructured":"Guanjun Lin Sheng Wen Qing-Long Han Jun Zhang and Yang Xiang. 2020. Software vulnerability detection using deep neural networks: a survey. Proc. IEEE 108 10 (2020) 1825\u20131848.","DOI":"10.1109\/JPROC.2020.2993293"},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"crossref","unstructured":"Guilong Lu Xiaolin Ju Xiang Chen Wenlong Pei and Zhilong Cai. 2024. GRACE: Empowering LLM-based software vulnerability detection with graph structure and in-context learning. Journal of Systems and Software 212 (2024) 112031.","DOI":"10.1016\/j.jss.2024.112031"},{"key":"e_1_3_3_2_31_2","unstructured":"Shuai Lu Daya Guo Shuo Ren Junjie Huang Alexey Svyatkovskiy Ambrosio Blanco Colin Clement Dawn Drain Daxin Jiang Duyu Tang et\u00a0al. 2021. Codexglue: A machine learning benchmark dataset for code understanding and generation. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2102.04664 (2021)."},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"crossref","unstructured":"Wei Ma Shangqing Liu Mengjie Zhao Xiaofei Xie Wenhang Wang Qiang Hu Jie Zhang and Yang Liu. 2024. Unveiling code pre-trained models: Investigating syntax and semantics capacities. ACM Transactions on Software Engineering and Methodology 33 7 (2024) 1\u201329.","DOI":"10.1145\/3664606"},{"key":"e_1_3_3_2_33_2","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1145\/3510454.3516865","volume-title":"Proceedings of the ACM\/IEEE 44th International Conference on Software Engineering: Companion Proceedings","author":"Nguyen Van-Anh","year":"2022","unstructured":"Van-Anh Nguyen, Dai\u00a0Quoc Nguyen, Van Nguyen, Trung Le, Quan\u00a0Hung Tran, and Dinh Phung. 2022. ReGVD: Revisiting graph neural networks for vulnerability detection. In Proceedings of the ACM\/IEEE 44th International Conference on Software Engineering: Companion Proceedings. 178\u2013182."},{"key":"e_1_3_3_2_34_2","unstructured":"Baptiste Roziere Jonas Gehring Fabian Gloeckle Sten Sootla Itai Gat Xiaoqing\u00a0Ellen Tan Yossi Adi Jingyu Liu Romain Sauvestre Tal Remez et\u00a0al. 2023. Code llama: Open foundation models for code. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2308.12950 (2023)."},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"crossref","unstructured":"Reuven Rubinstein. 1999. The cross-entropy method for combinatorial and continuous optimization. Methodology and computing in applied probability 1 (1999) 127\u2013190.","DOI":"10.1023\/A:1010091220143"},{"key":"e_1_3_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2018.00120"},{"key":"e_1_3_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3597503.3623345"},{"key":"e_1_3_3_2_38_2","doi-asserted-by":"crossref","unstructured":"Mingwei Tang Wei Tang Qingchi Gui Jie Hu and Mingfeng Zhao. 2024. A vulnerability detection algorithm based on residual graph attention networks for source code imbalance (RGAN). Expert Systems with Applications 238 (2024) 122216.","DOI":"10.1016\/j.eswa.2023.122216"},{"key":"e_1_3_3_2_39_2","doi-asserted-by":"crossref","unstructured":"Wei Tang Mingwei Tang Minchao Ban Ziguo Zhao and Mingjun Feng. 2023. CSGVD: A deep learning approach combining sequence and graph embedding for source code vulnerability detection. Journal of Systems and Software 199 (2023) 111623.","DOI":"10.1016\/j.jss.2023.111623"},{"key":"e_1_3_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3564625.3567985"},{"key":"e_1_3_3_2_41_2","first-page":"2275","volume-title":"2023 IEEE\/ACM 45th International Conference on Software Engineering (ICSE)","author":"Wen Xin-Cheng","year":"2023","unstructured":"Xin-Cheng Wen, Yupan Chen, Cuiyun Gao, Hongyu Zhang, Jie\u00a0M Zhang, and Qing Liao. 2023. Vulnerability detection with graph simplification and enhanced graph representation learning. In 2023 IEEE\/ACM 45th International Conference on Software Engineering (ICSE). IEEE, 2275\u20132286."},{"key":"e_1_3_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3650212.3652124"},{"key":"e_1_3_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3650212.3652124"},{"key":"e_1_3_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510229"},{"key":"e_1_3_3_2_45_2","doi-asserted-by":"crossref","unstructured":"Xin Yin Chao Ni and Shaohua Wang. 2024. Multitask-based evaluation of open-source llm on software vulnerability. IEEE Transactions on Software Engineering (2024).","DOI":"10.1109\/TSE.2024.3470333"},{"key":"e_1_3_3_2_46_2","doi-asserted-by":"crossref","unstructured":"Junwei Zhang Zhongxin Liu Xing Hu Xin Xia and Shanping Li. 2023. Vulnerability detection by learning from syntax-based execution paths of code. IEEE Transactions on Software Engineering 49 8 (2023) 4196\u20134212.","DOI":"10.1109\/TSE.2023.3286586"},{"key":"e_1_3_3_2_47_2","unstructured":"Ye Zhang and Byron Wallace. 2015. A sensitivity analysis of (and practitioners\u2019 guide to) convolutional neural networks for sentence classification. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1510.03820 (2015)."},{"key":"e_1_3_3_2_48_2","unstructured":"Yaqin Zhou Shangqing Liu Jingkai Siow Xiaoning Du and Yang Liu. 2019. Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks. Advances in neural information processing systems 32 (2019)."}],"event":{"name":"Internetware 2025: the 16th International Conference on Internetware","location":"Trondheim Norway","acronym":"Internetware 2025","sponsor":["SIGSOFT ACM Special Interest Group on Artificial Intelligence"]},"container-title":["Proceedings of the 16th International Conference on Internetware"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3755881.3755907","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T11:52:22Z","timestamp":1761565942000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3755881.3755907"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,20]]},"references-count":47,"alternative-id":["10.1145\/3755881.3755907","10.1145\/3755881"],"URL":"https:\/\/doi.org\/10.1145\/3755881.3755907","relation":{},"subject":[],"published":{"date-parts":[[2025,6,20]]},"assertion":[{"value":"2025-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}