{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T03:05:26Z","timestamp":1769742326190,"version":"3.49.0"},"reference-count":42,"publisher":"Institution of Engineering and Technology (IET)","issue":"2","license":[{"start":{"date-parts":[[2021,12,24]],"date-time":"2021-12-24T00:00:00Z","timestamp":1640304000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Software"],"published-print":{"date-parts":[[2022,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Cross\u2010project defect prediction (CPDP) is an important research direction in software defect prediction. Traditional CPDP methods based on hand\u2010crafted features ignore the semantic information in the source code. Existing CPDP methods based on the\u00a0deep learning model may not fully consider the differences among projects. Additionally, these methods may not accurately classify the samples near the classification boundary. To solve these problems, the authors propose a model based on multi\u2010adaptation and nuclear norm (MANN) to deal with samples in projects. The feature of samples were embedded into the multi\u2010core Hilbert space for distribution and the multi\u2010kernel maximum mean discrepancy method was utilised to reduce differences among projects. More importantly, the nuclear norm module was constructed, which improved the discriminability and diversity of the target sample by calculating and maximizing the nuclear norm of the target sample in the process of domain adaptation, thus improving the performance of MANN. Finally, extensive experiments were conducted on 11 sizeable open\u2010source projects. The results indicate\u00a0that the proposed method exceeds the state of the art under the widely used metrics.<\/jats:p>","DOI":"10.1049\/sfw2.12053","type":"journal-article","created":{"date-parts":[[2021,12,25]],"date-time":"2021-12-25T01:53:50Z","timestamp":1640397230000},"page":"200-213","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A cross\u2010project defect prediction method based on multi\u2010adaptation and nuclear norm"],"prefix":"10.1049","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8911-8707","authenticated-orcid":false,"given":"Qingan","family":"Huang","sequence":"first","affiliation":[{"name":"School of Software Engineering South China University of Technology  Guangzhou China"},{"name":"Guangzhou City University of Technology  Guangzhou China"}]},{"given":"Le","family":"Ma","sequence":"additional","affiliation":[{"name":"Guangzhou City University of Technology  Guangzhou China"}]},{"given":"Siyu","family":"Jiang","sequence":"additional","affiliation":[{"name":"Guangzhou Key Laboratory of Multilingual Intelligent Processing School of Information Science and Technology Guangdong University of Foreign Studies  Guangzhou China"}]},{"given":"Guobin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Software Engineering South China University of Technology  Guangzhou China"},{"name":"Guangzhou City University of Technology  Guangzhou China"}]},{"given":"Hengjie","family":"Song","sequence":"additional","affiliation":[{"name":"School of Software Engineering South China University of Technology  Guangzhou China"}]},{"given":"Libiao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Guangzhou City University of Technology  Guangzhou China"}]},{"given":"Chunyun","family":"Zheng","sequence":"additional","affiliation":[{"name":"Automotive Engineering Research Institute Guangzhou Automobile Group Co., Ltd  Guangzhou China"}]}],"member":"265","published-online":{"date-parts":[[2021,12,24]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2014.2322358"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2014.07.005"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-sen.2017.0148"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-sen.2020.0119"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2018.2791521"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2016.2597849"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/app9102138"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-008-9103-7"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1595696.1595713"},{"key":"e_1_2_10_11_1","doi-asserted-by":"crossref","unstructured":"Sun Y. et\u00a0al.:Manifold embedded distribution adaptation for cross\u2010project defect prediction 14(7) 825\u2010838(2021)","DOI":"10.1049\/iet-sen.2019.0389"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2018.2877612"},{"key":"e_1_2_10_13_1","volume-title":"SEKE","author":"Wang Z.","year":"2020"},{"key":"e_1_2_10_14_1","unstructured":"Yosinski J. et\u00a0al.:How transferable are features in deep neural networks?(2014)"},{"key":"e_1_2_10_15_1","volume-title":"International Conference on Machine Learning (PMLR)","author":"Zhang K.","year":"2013"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00400"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00218"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00262"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58555-6_42"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2011.09.007"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2008.11.007"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2013.6606584"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2091281"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2020.3001739"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11390-019-1959-z"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2021.3074660"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2884781.2884804"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2925313"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/QRS.2017.42"},{"key":"e_1_2_10_30_1","doi-asserted-by":"publisher","DOI":"10.3390\/app9132660"},{"key":"e_1_2_10_31_1","volume-title":"International Conference on Machine Learning","author":"Long M.","year":"2015"},{"key":"e_1_2_10_32_1","volume-title":"Advances in Neural Information Processing Systems","author":"Gretton A.","year":"2012"},{"key":"e_1_2_10_33_1","volume-title":"CAP","author":"Grandvalet Y.","year":"2005"},{"key":"e_1_2_10_34_1","doi-asserted-by":"crossref","unstructured":"Miyato T. et\u00a0al.:Virtual adversarial training: a regularization method for supervised and semi\u2010supervised learning 41 (8) pp.1979\u20101993. (2018)","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"e_1_2_10_35_1","unstructured":"Long M. et\u00a0al.:Unsupervised domain adaptation with residual transfer networks (2016)"},{"key":"e_1_2_10_36_1","volume-title":"Matrix Rank Minimization with Applications, PhD Thesis","author":"Fazel M.","year":"2002"},{"key":"e_1_2_10_37_1","doi-asserted-by":"crossref","unstructured":"Recht B. Fazel M. andParrilo P.A.J.S.r. Guaranteed minimum\u2010rank solutions of linear matrix equations via nuclear norm minimization SIAM Rev.52 (3) pp.471\u2010501. (2010)","DOI":"10.1137\/070697835"},{"key":"e_1_2_10_38_1","volume-title":"Advances in Neural Information Processing Systems","author":"Srebro N.","year":"2005"},{"key":"e_1_2_10_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2016.2543218"},{"key":"e_1_2_10_40_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001419590377"},{"key":"e_1_2_10_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2016.2584050"},{"key":"e_1_2_10_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01155"},{"issue":"1","key":"e_1_2_10_43_1","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/TCBB.2019.2963873","article-title":"A novel negative\u2010transfer\u2010resistant fuzzy clustering model with a shared cross\u2010domain transfer latent space and its application to brain CT image segmentation","volume":"18","author":"Jiang Y.","year":"2020","journal-title":"IEEE ACM Trans. Comput. Biol. Bioinf"}],"container-title":["IET Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/sfw2.12053","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/sfw2.12053","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/sfw2.12053","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T19:18:53Z","timestamp":1761679133000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/sfw2.12053"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,24]]},"references-count":42,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["10.1049\/sfw2.12053"],"URL":"https:\/\/doi.org\/10.1049\/sfw2.12053","archive":["Portico"],"relation":{},"ISSN":["1751-8806"],"issn-type":[{"value":"1751-8806","type":"print"}],"subject":[],"published":{"date-parts":[[2021,12,24]]},"assertion":[{"value":"2021-07-13","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-06","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}