{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T19:16:05Z","timestamp":1789413365775,"version":"build-2803163510"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91118005"],"award-info":[{"award-number":["91118005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Macao Special Project of the State Ministry of Science and Technology","award":["2015DFM10020"],"award-info":[{"award-number":["2015DFM10020"]}]},{"name":"Graduate Research and Innovation Foundation of Chongqing, China","award":["CYS18006"],"award-info":[{"award-number":["CYS18006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2018.2889061","type":"journal-article","created":{"date-parts":[[2018,12,21]],"date-time":"2018-12-21T20:32:00Z","timestamp":1545424320000},"page":"7663-7677","source":"Crossref","is-referenced-by-count":47,"title":["Siamese Dense Neural Network for Software Defect Prediction With Small Data"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6502-587X","authenticated-orcid":false,"given":"Linchang","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaowei","family":"Shang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anyong","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan Yan","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007735"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2009.4960642"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.11.024"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2013.11"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1049\/iet-sen.2011.0132"},{"key":"ref30","first-page":"641","article-title":"Investigating Siamese LSTM networks for text categorization","author":"shih","year":"2018","journal-title":"Proc Asia–Pacific Signal Inf Process Assoc Summit Conf"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2017.2754998"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s11334-017-0295-0"},{"key":"ref35","first-page":"13","article-title":"Underwater pipelines panoramic image transmission and refinement using acoustic sensors","volume":"16","author":"jindal","year":"2017","journal-title":"Int J Wavelets Multiresolution Inf Process"},{"key":"ref34","first-page":"807","article-title":"In defense of fully connected layers in visual representation transfer","volume":"36","author":"zhang","year":"2017","journal-title":"Proc Adv Multimedia Inf Process (PCM)"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2749964"},{"key":"ref27","article-title":"Zero-shot learning—A comprehensive evaluation of the good, the bad and the ugly","author":"xian","year":"0","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref29","first-page":"378","article-title":"Siamese network features for image matching","author":"melekhov","year":"2017","journal-title":"Proc Int Conf Pattern Recognit"},{"key":"ref2","first-page":"1","article-title":"Learning to classify software defects from crowds: A novel approach","volume":"62","author":"hern\u00e1ndez-gonz\u00e1lez","year":"2017","journal-title":"Appl Soft Comput"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2017.06.070"},{"key":"ref20","author":"majumder","year":"2018","journal-title":"500+ times faster than deep learning (a case study exploring faster methods for text mining StackOverflow)"},{"key":"ref22","first-page":"1","article-title":"Learning to remember rare events","author":"kaiser","year":"2016","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref21","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref24","first-page":"1","article-title":"Learning feed-forward one-shot learners","author":"bertinetto","year":"2016","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.79"},{"key":"ref26","first-page":"1","article-title":"Prototypical networks for few-shot learning","author":"snell","year":"2017","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref25","first-page":"1","article-title":"Towards a neural statistician","author":"edwards","year":"2016","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref50","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1016\/j.infsof.2007.02.015","article-title":"A systematic review of effect size in software engineering experiments","volume":"49","author":"kampenes","year":"2007","journal-title":"Inf Softw Technol"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2007.256941"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/2884781.2884857"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1080\/03610920802187448"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.1976.233837"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/2222444.2222446"},{"key":"ref40","first-page":"2786","article-title":"Siamese recurrent architectures for learning sentence similarity","author":"mueller","year":"2016","journal-title":"Proc 30th AAAI Conf Artif Intell"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1587\/transinf.2015EDP7107"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/QRS.2016.31"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/QRS.2017.42"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref16","first-page":"78","article-title":"Software bug prediction using machine learning approach","volume":"9","author":"hammouri","year":"2018","journal-title":"Int J Adv Comput Sci Appl"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299064"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3106237.3106256"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2015.01.014"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2017.11.008"},{"key":"ref6","author":"sung","year":"2017","journal-title":"Learning to compare Relation network for few-shot learning"},{"key":"ref5","article-title":"Siamese neural networks for one-shot image recognition","author":"koch","year":"2015"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W16-1617"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001493000339"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.2307\/3001968"},{"key":"ref9","first-page":"773","article-title":"Vehicle verification based on deep siamese network with similarity metric","author":"zhang","year":"2017","journal-title":"Proc Pacific Rim Conf Multimedia"},{"key":"ref46","author":"dam","year":"2018","journal-title":"A deep tree-based model for software defect prediction"},{"key":"ref45","first-page":"1","article-title":"Deep neural network based hybrid approach for software defect prediction using software metrics","volume":"3","author":"manjula","year":"2018","journal-title":"Cluster Comput"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.14257\/ijseia.2013.7.5.16"},{"key":"ref47","first-page":"229","article-title":"Deep belief network software defect prediction model","volume":"44","author":"lu","year":"2017","journal-title":"Comput Sci"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.202"},{"key":"ref41","first-page":"1","article-title":"Loss functions for discriminative training of energy-based models","author":"lecun","year":"2005","journal-title":"Proc 10th Int Workshop Artif Intell Statist"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2014.2322358"},{"key":"ref43","first-page":"1","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"Comput Sci"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08585009.pdf?arnumber=8585009","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T00:12:13Z","timestamp":1643242333000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8585009\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/access.2018.2889061","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]}}}