{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T15:52:38Z","timestamp":1781193158576,"version":"3.54.1"},"reference-count":106,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T00:00:00Z","timestamp":1719446400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"MUR","award":["2020W3A5FY"],"award-info":[{"award-number":["2020W3A5FY"]}]},{"name":"SERICS","award":["PE00000014"],"award-info":[{"award-number":["PE00000014"]}]},{"name":"NRRP MUR"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>\n            With the rate of discovered and disclosed vulnerabilities escalating, researchers have been experimenting with machine learning to predict whether a vulnerability will be exploited. Existing solutions leverage information unavailable when a CVE is created, making them unsuitable just after the disclosure. This paper experiments with\n            <jats:italic>early<\/jats:italic>\n            exploitability prediction models driven exclusively by the initial CVE record, i.e., the original description and the linked online discussions. Leveraging NVD and Exploit Database, we evaluate 72 prediction models trained using six traditional machine learning classifiers, four feature representation schemas, and three data balancing algorithms. We also experiment with five pre-trained large language models (LLMs). The models leverage seven different corpora made by combining three data sources, i.e., CVE description,\n            <jats:sc>Security Focus<\/jats:sc>\n            , and\n            <jats:sc>BugTraq<\/jats:sc>\n            . The models are evaluated in a\n            <jats:italic>realistic<\/jats:italic>\n            , time-aware fashion by removing the training and test instances that cannot be labeled\n            <jats:italic>\u201cneutral\u201d<\/jats:italic>\n            \u00a0with sufficient confidence. The validation reveals that CVE descriptions and\n            <jats:sc>Security Focus<\/jats:sc>\n            discussions are the best data to train on. Pre-trained LLMs do not show the expected performance, requiring further pre-training in the security domain. We distill new research directions, identify possible room for improvement, and envision automated systems assisting security experts in assessing the exploitability.\n          <\/jats:p>","DOI":"10.1145\/3654443","type":"journal-article","created":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T11:57:28Z","timestamp":1711540648000},"page":"1-41","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Early and Realistic Exploitability Prediction of Just-Disclosed Software Vulnerabilities: How Reliable Can It Be?"],"prefix":"10.1145","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7489-9969","authenticated-orcid":false,"given":"Emanuele","family":"Iannone","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Salerno, Fisciano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5491-0873","authenticated-orcid":false,"given":"Giulia","family":"Sellitto","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Salerno, Fisciano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1319-7604","authenticated-orcid":false,"given":"Emanuele","family":"Iaccarino","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Salerno, Fisciano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0975-8972","authenticated-orcid":false,"given":"Filomena","family":"Ferrucci","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Salerno, Fisciano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4238-1425","authenticated-orcid":false,"given":"Andrea","family":"De Lucia","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Salerno, Fisciano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9337-5116","authenticated-orcid":false,"given":"Fabio","family":"Palomba","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Salerno, Fisciano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,6,27]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2630069"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/CYCONUS.2017.8167501"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00799-014-0111-5"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISCC50000.2020.9219568"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10796-006-9012-5"},{"key":"e_1_3_2_7_2","volume-title":"Proc. of USENIX Security Symposium","author":"Arp Daniel","year":"2020","unstructured":"Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, and Konrad Rieck. 2020. Dos and don\u2019ts of machine learning in computer security. In Proc. of USENIX Security Symposium. http:\/\/arxiv.org\/abs\/2010.09470"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/SYNASC.2017.00035"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.5555\/553876"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/16.5.412"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/1370750.1370757"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSM.2009.5306383"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1002\/qre.2754"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/2382196.2382284"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835821"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_2_17_2","volume-title":"Classification and Regression Trees Regression Trees","author":"Breiman Leo","year":"1984","unstructured":"Leo Breiman, Jerome Friedman, Charles J. Stone, and R. A. Olshen. 1984. Classification and Regression Trees Regression Trees. Chapman and Hall."},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3041008.3041009"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2020.102067"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2007.26"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3338906.3338947"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.5555\/1622407.1622416"},{"key":"e_1_3_2_23_2","first-page":"786","article-title":"A maximum entropy approach to information extraction from semi-structured and free text","volume":"2002","author":"Chieu Hai Leong","year":"2002","unstructured":"Hai Leong Chieu and Hwee Tou Ng. 2002. A maximum entropy approach to information extraction from semi-structured and free text. AAAI\/IAAI 2002 (2002), 786\u2013791.","journal-title":"AAAI\/IAAI"},{"key":"e_1_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Alexis Conneau Kartikay Khandelwal Naman Goyal Vishrav Chaudhary Guillaume Wenzek Francisco Guzm\u00e1n Edouard Grave Myle Ott Luke Zettlemoyer and Veselin Stoyanov. 2020. Unsupervised Cross-Lingual Representation Learning at Scale. arxiv:1911.02116 [cs.CL].","DOI":"10.18653\/v1\/2020.acl-main.747"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/SANER53432.2022.00050"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2012.08.002"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/REW53955.2021.00020"},{"key":"e_1_3_2_30_2","unstructured":"Jacob Devlin Ming-Wei Chang Kenton Lee and Kristina N. Toutanova. 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. https:\/\/arxiv.org\/abs\/1810.04805"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2021.106771"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1002\/aris.1440380105"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/2663716.2663755"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF02288367"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.3233\/978-1-61499-589-0-48"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3407023.3407038"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","unstructured":"Michael Felderer Matthias B\u00fcchler Martin Johns Achim D. Brucker Ruth Breu and Alexander Pretschner. 2016. Chapter one - security testing: A survey. Advances in Computers Vol. 101. Elsevier 1\u201351. 10.1016\/bs.adcom.2015.11.003","DOI":"10.1016\/bs.adcom.2015.11.003"},{"key":"e_1_3_2_38_2","doi-asserted-by":"crossref","unstructured":"Zhangyin Feng Daya Guo Duyu Tang Nan Duan Xiaocheng Feng Ming Gong Linjun Shou Bing Qin Ting Liu Daxin Jiang and Ming Zhou. 2020. CodeBERT: A Pre-Trained Model for Programming and Natural Languages. arxiv:2002.08155 [cs.CL].","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/1162666.1162671"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-6967-5_6"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177731944"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/ESEM.2009.5314230"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","unstructured":"Aayush Garg Renzo Degiovanni Matthieu Jimenez Maxime Cordy Mike Papadakis and Yves LeTraon. 2018. Learning from what we know: How to perform vulnerability prediction using noisy historical data. Empirical Software Engineering 27 169 (2022). 10.1007\/s10664-022-10197-4","DOI":"10.1007\/s10664-022-10197-4"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2023.107217"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2022.11.001"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSME.2017.52"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2012.66"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASE51524.2021.9678905"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2022.3140868"},{"key":"e_1_3_2_50_2","doi-asserted-by":"crossref","unstructured":"Emanuele Iannone Giulia Sellitto Emanuele Iaccarino Filomena Ferrucci Andrea De Lucia and Fabio Palomba. 2023. Early and Realistic Exploitability Prediction of Just-Disclosed Software Vulnerabilities: How Reliable Can it Be? \u2013 Online Appendix. https:\/\/figshare.com\/s\/165b39c7094c7831365e.","DOI":"10.1145\/3654443"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1093\/cybsec\/tyaa015"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3436242"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSPW59978.2023.00027"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3560575"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3338906.3338941"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.3233\/IFS-151733"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-017-9521-5"},{"key":"e_1_3_2_58_2","unstructured":"Zhenzhong Lan Mingda Chen Sebastian Goodman Kevin Gimpel Piyush Sharma and Radu Soricut. 2020. ALBERT: A Lite BERT for Self-Supervised Learning of Language Representations. arxiv:1909.11942 [cs.CL]."},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/161494.161501"},{"key":"e_1_3_2_60_2","first-page":"1188","volume-title":"International Conference on Machine Learning","author":"Le Quoc","year":"2014","unstructured":"Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In International Conference on Machine Learning. PMLR, 1188\u20131196."},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASE51524.2021.9678622"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383219.3383225"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2018.2834476"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383219.3383222"},{"key":"e_1_3_2_65_2","unstructured":"Yinhan Liu Myle Ott Naman Goyal Jingfei Du Mandar Joshi Danqi Chen Omer Levy Mike Lewis Luke Zettlemoyer and Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. arxiv:1907.11692 [cs.CL]."},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2022.111283"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASE52547.2021.00014"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/1853919.1853925"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1016\/0005-2795(75)90109-9"},{"key":"e_1_3_2_70_2","volume-title":"Software Security: Building Security in","author":"McGraw G.","year":"2006","unstructured":"G. McGraw. 2006. Software Security: Building Security in. Addison-Wesley. 2005031598"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.4135\/9781412983433"},{"key":"e_1_3_2_72_2","article-title":"Exploiting Similarities among Languages for Machine Translation","volume":"1309","author":"Mikolov Tom\u00e1s","year":"2013","unstructured":"Tom\u00e1s Mikolov, Quoc V. Le, and Ilya Sutskever. 2013. Exploiting Similarities among Languages for Machine Translation. CoRR abs\/1309.4168 (2013). arXiv:1309.4168. http:\/\/arxiv.org\/abs\/1309.4168","journal-title":"CoRR"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1145\/2746194.2746198"},{"key":"e_1_3_2_74_2","volume-title":"Distribution-Free Multiple Comparisons","author":"Nemenyi Peter Bjorn","year":"1963","unstructured":"Peter Bjorn Nemenyi. 1963. Distribution-Free Multiple Comparisons. Princeton University."},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2020.110693"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","unstructured":"Chanathip Pornprasit and Chakkrit Tantithamthavorn. 2021. JITLine: A simpler better faster finer-grained just-in-time defect prediction. 369\u2013379. 10.1109\/MSR52588.2021.00049","DOI":"10.1109\/MSR52588.2021.00049"},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1108\/eb046814"},{"key":"e_1_3_2_78_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-011-0012-5"},{"key":"e_1_3_2_79_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2017.2755005"},{"key":"e_1_3_2_80_2","unstructured":"John Ratcliff and David Metzener. 1988. Pattern Matching: the Gestalt Approach. https:\/\/www.drdobbs.com\/database\/pattern-matching-the-gestalt-approach\/184407970?pgno=5Accessed: 2022-03-125."},{"key":"e_1_3_2_81_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-03638-6_21"},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1109\/SCAM.2015.7335423"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1145\/345099.345137"},{"key":"e_1_3_2_84_2","first-page":"41","volume-title":"IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence","author":"Rish Irina","year":"2001","unstructured":"Irina Rish. 2001. An empirical study of the naive Bayes classifier. In IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, Vol. 3. 41\u201346."},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.1002\/9780470689646.ch1"},{"key":"e_1_3_2_86_2","series-title":"SEC\u201915","first-page":"1041","volume-title":"Proceedings of the 24th USENIX Conference on Security Symposium","author":"Sabottke Carl","year":"2015","unstructured":"Carl Sabottke, Octavian Suciu, and Tudor Dumitra\u015f. 2015. Vulnerability disclosure in the age of social media: Exploiting Twitter for predicting real-world exploits. In Proceedings of the 24th USENIX Conference on Security Symposium (Washington, D.C.) (SEC\u201915). USENIX Association, USA, 1041\u20131056."},{"key":"e_1_3_2_87_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-019-09754-1"},{"key":"e_1_3_2_88_2","unstructured":"Victor Sanh Lysandre Debut Julien Chaumond and Thomas Wolf. 2020. DistilBERT a Distilled Version of BERT: Smaller Faster Cheaper and Lighter. arxiv:1910.01108 [cs.CL]."},{"key":"e_1_3_2_89_2","article-title":"Early detection of security-relevant bug reports using machine learning: How far are we?","volume":"2112","author":"Sawadogo Arthur D.","year":"2021","unstructured":"Arthur D. Sawadogo, Quentin Guimard, Tegawend\u00e9 F. Bissyand\u00e9, Abdoul Kader Kabor\u00e9, Jacques Klein, and Naouel Moha. 2021. Early detection of security-relevant bug reports using machine learning: How far are we? CoRR abs\/2112.10123 (2021). arXiv:2112.10123. https:\/\/arxiv.org\/abs\/2112.10123","journal-title":"CoRR"},{"key":"e_1_3_2_90_2","doi-asserted-by":"publisher","DOI":"10.1002\/smr.1958"},{"key":"e_1_3_2_91_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSR.2010.5463280"},{"key":"e_1_3_2_92_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2003.1223656"},{"key":"e_1_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-020-09882-z"},{"key":"e_1_3_2_94_2","first-page":"377","volume-title":"31st USENIX Security Symposium (USENIX Security 22)","author":"Suciu Octavian","year":"2022","unstructured":"Octavian Suciu, Connor Nelson, Zhuoer Lyu, Tiffany Bao, and Tudor Dumitras. 2022. Expected exploitability: Predicting the development of functional vulnerability exploits. In 31st USENIX Security Symposium (USENIX Security 22). USENIX Association, Boston, MA, 377\u2013394. https:\/\/www.usenix.org\/conference\/usenixsecurity22\/presentation\/suciu"},{"key":"e_1_3_2_95_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2019.106204"},{"key":"e_1_3_2_96_2","article-title":"The goal question metric approach","author":"V. R. Basili Victor R.","year":"1994","unstructured":"Victor R. V. R. Basili, Gianluigi Caldiera, and H. Dieter Rombach. 1994. The goal question metric approach. Encyclopedia of Software Engineering (1994).","journal-title":"Encyclopedia of Software Engineering"},{"key":"e_1_3_2_97_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACSAC.2000.898880"},{"key":"e_1_3_2_98_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-015-0448-4"},{"key":"e_1_3_2_99_2","unstructured":"Yonghui Wu Mike Schuster Zhifeng Chen Quoc V. Le Mohammad Norouzi Wolfgang Macherey Maxim Krikun Yuan Cao Qin Gao Klaus Macherey Jeff Klingner Apurva Shah Melvin Johnson Xiaobing Liu \u0141ukasz Kaiser Stephan Gouws Yoshikiyo Kato Taku Kudo Hideto Kazawa Keith Stevens George Kurian Nishant Patil Wei Wang Cliff Young Jason Smith Jason Riesa Alex Rudnick Oriol Vinyals Greg Corrado Macduff Hughes and Jeffrey Dean. 2016. Google\u2019s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. arxiv:1609.08144 [cs.CL]."},{"key":"e_1_3_2_100_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383219.3383232"},{"key":"e_1_3_2_101_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106529"},{"key":"e_1_3_2_102_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.01.144"},{"key":"e_1_3_2_103_2","volume-title":"Proceedings of the ICML\u20192003 Workshop on Learning from Imbalanced Datasets","author":"Zhang Jianping","year":"2003","unstructured":"Jianping Zhang and Inderjeet Mani. 2003. KNN approach to unbalanced data distributions: A case study involving information extraction. In Proceedings of the ICML\u20192003 Workshop on Learning from Imbalanced Datasets."},{"key":"e_1_3_2_104_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICECCS.2015.15"},{"key":"e_1_3_2_105_2","doi-asserted-by":"publisher","DOI":"10.21037\/atm.2016.03.37"},{"key":"e_1_3_2_106_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASE51524.2021.9678720"},{"key":"e_1_3_2_107_2","doi-asserted-by":"publisher","DOI":"10.1002\/smr.1770"}],"container-title":["ACM Transactions on Software Engineering and Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3654443","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3654443","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T23:57:14Z","timestamp":1750291034000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3654443"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,27]]},"references-count":106,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,7,31]]}},"alternative-id":["10.1145\/3654443"],"URL":"https:\/\/doi.org\/10.1145\/3654443","relation":{},"ISSN":["1049-331X","1557-7392"],"issn-type":[{"value":"1049-331X","type":"print"},{"value":"1557-7392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,27]]},"assertion":[{"value":"2023-11-11","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-03-06","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}