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Learning prediction model with such insufficient training data will limit the efficacy of learned predictor. In practice, there are usually many publicly available fault prediction datasets. Recently, heterogeneous fault prediction (HFP) has been proposed. However, existing HFP models do not investigate how to use mixed project data to predict target. Furthermore, defect data are often imbalanced. The imbalanced data distribution of source usually leads to serious misclassification of fault\u2010prone instances, which will degrade the predictor's performance. Existing HFP methods do not consider the class imbalance problem in the training stages. In this paper, we propose a novel Cost\u2010sensitive Label and Structure\u2010consistent Unilateral Projection (CLSUP) approach for HFP. CLSUP can not only make better use of the within\u2010project and cross\u2010project data but also alleviate the class imbalance problem by setting different misclassification costs for fault\u2010prone and non\u2013fault\u2010prone instances. Extensive experiments on 30 projects demonstrate the effectiveness of CLSUP.<\/jats:p>","DOI":"10.1002\/stvr.1658","type":"journal-article","created":{"date-parts":[[2018,1,26]],"date-time":"2018-01-26T08:57:49Z","timestamp":1516957069000},"source":"Crossref","is-referenced-by-count":29,"title":["Heterogeneous fault prediction with cost\u2010sensitive domain adaptation"],"prefix":"10.1002","volume":"28","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-5999-3658","authenticated-orcid":false,"given":"Zhiqiang","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Software Engineering, School of Computer Wuhan University Wuhan 430072 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-0392-8475","authenticated-orcid":false,"given":"Xiao\u2010Yuan","family":"Jing","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Software Engineering, School of Computer Wuhan University Wuhan 430072 China"},{"name":"School of Automation Nanjing University of Posts and Telecommunications Nanjing 210023 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoke","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Software Engineering, School of Computer Wuhan University Wuhan 430072 China"},{"name":"School of Computer and Information Engineering Henan University Kaifeng 475001 China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,1,26]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2007.256941"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2007.70773"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2007.07.040"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/stvr.1570"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/stvr.1610"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10515-010-0069-5"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2011.103"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2012.83"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2014.2322358"},{"issue":"11","key":"e_1_2_8_11_1","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1016\/j.infsof.2010.06.006","article-title":"Practical considerations in deploying statistical methods for defect prediction: A case study within the turkish telecommunications industry","volume":"52","author":"Tosun A.","year":"2010","journal-title":"Inf. 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