{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T02:42:52Z","timestamp":1784342572105,"version":"3.55.0"},"reference-count":54,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"8","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IIEEE Trans. Software Eng."],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1109\/tse.2024.3423712","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T17:17:07Z","timestamp":1720199827000},"page":"2163-2177","source":"Crossref","is-referenced-by-count":22,"title":["Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasets"],"prefix":"10.1109","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5965-615X","authenticated-orcid":false,"given":"Partha","family":"Chakraborty","sequence":"first","affiliation":[{"name":"David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2309-0203","authenticated-orcid":false,"given":"Krishna Kanth","family":"Arumugam","sequence":"additional","affiliation":[{"name":"David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2621-6104","authenticated-orcid":false,"given":"Mahmoud","family":"Alfadel","sequence":"additional","affiliation":[{"name":"David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4533-4728","authenticated-orcid":false,"given":"Meiyappan","family":"Nagappan","sequence":"additional","affiliation":[{"name":"David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0193-3975","authenticated-orcid":false,"given":"Shane","family":"McIntosh","sequence":"additional","affiliation":[{"name":"David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"ref2","article-title":"Towards sparse hierarchical graph classifiers","author":"Cangea","year":"2018"},{"key":"ref3","article-title":"RLocator: Reinforcement learning for bug localization","author":"Chakraborty","year":"2023"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2021.3087402"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3436877"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICECCS.2019.00012"},{"key":"ref9","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"Chung","year":"2014"},{"key":"ref10","article-title":"CWE - CWE-888: Software fault pattern (SFP) clusters (4.12)","year":"2023"},{"key":"ref11","article-title":"QLoRA: Efficient finetuning of quantized LLMs","author":"Dettmers","year":"2023"},{"key":"ref12","article-title":"Vulnerability detection with code language models: How far are we?","author":"Ding","year":"2024"},{"key":"ref13","article-title":"Mixtral of experts","author":"Jiang","year":"2024"},{"key":"ref14","article-title":"Code llama: Open foundation models for code","author":"Rozi\u00e8re","year":"2024"},{"key":"ref15","article-title":"Llama 2: Open foundation and fine-tuned chat models","author":"Touvron","year":"2023"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3379597.3387501"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"ref18","first-page":"148","article-title":"Experiments with a new boosting algorithm","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"96","author":"Freund","year":"1996"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.2307\/2699986"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3524842.3528452"},{"key":"ref21","first-page":"218","article-title":"An analysis of C\/C++ datasets for machine learning-assisted software vulnerability detection","volume-title":"Proc. Conf. Appl. Mach. Learn. Inf. Secur.","author":"Grahn","year":"2021"},{"key":"ref22","volume-title":"Neural Network Design","author":"Hagan","year":"1997"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/5254.708428"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref25","article-title":"Comparing the performance of LLMs: A deep dive into roberta, Llama 2, and mistral for disaster tweets analysis with Lora","volume-title":"Nov.","author":"Iraqi","year":"2023"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04898-2_327"},{"key":"ref27","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"key":"ref28","article-title":"Convolutional networks for images, speech, and time series","volume-title":"The Handbook of Brain Theory and Neural Networks","author":"LeCun","year":"1995"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/QRS.2017.42"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3468597"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2021.3051525"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23158"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.2993293"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2022.111283"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3457607"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.430"},{"key":"ref37","first-page":"527","article-title":"The beauty and the beast: Vulnerabilities in Red Hats packages","volume-title":"Proc. USENIX Annu. Tech. Conf.","author":"Neuhaus","year":"2009"},{"key":"ref38","article-title":"NIST software assurance reference dataset","year":"2023"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.4249\/scholarpedia.1883"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/234313.234346"},{"key":"ref41","first-page":"324","article-title":"Data augmentation for text classification with EASE","volume-title":"Proc. 6th Int. Conf. Natural Lang. Speech Process. (ICNLSP)","author":"Rahman","year":"2023"},{"key":"ref42","article-title":"Data augmentation can improve robustness","author":"Rebuffi","year":"2021"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-3020"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2018.00120"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2014.2340398"},{"issue":"11","key":"ref46","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3379597.3387465"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510050"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3052951"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3520312.3534862"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3134600.3134620"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2020.110659"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE-SEIP52600.2021.00020"},{"key":"ref54","article-title":"Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Zhou","year":"2019"}],"container-title":["IEEE Transactions on Software Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/32\/10636961\/10587162.pdf?arnumber=10587162","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T19:43:12Z","timestamp":1723837392000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10587162\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":54,"journal-issue":{"issue":"8"},"URL":"https:\/\/doi.org\/10.1109\/tse.2024.3423712","relation":{},"ISSN":["0098-5589","1939-3520","2326-3881"],"issn-type":[{"value":"0098-5589","type":"print"},{"value":"1939-3520","type":"electronic"},{"value":"2326-3881","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8]]}}}