{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T02:50:14Z","timestamp":1726455014694},"reference-count":16,"publisher":"Elsevier","isbn-type":[{"type":"print","value":"9780128042069"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1016\/b978-0-12-804206-9.00030-1","type":"book-chapter","created":{"date-parts":[[2016,7,22]],"date-time":"2016-07-22T16:16:31Z","timestamp":1469204191000},"page":"155-159","source":"Crossref","is-referenced-by-count":2,"title":["Which machine learning method do you need?"],"prefix":"10.1016","author":[{"given":"L.L.","family":"Minku","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/B978-0-12-804206-9.00030-1_bb0010","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1109\/TSE.2011.103","article-title":"A systematic literature review on fault prediction performance in software engineering","volume":"38","author":"Hall","year":"2012","journal-title":"IEEE Trans Softw Eng"},{"issue":"2","key":"10.1016\/B978-0-12-804206-9.00030-1_bb0015","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1109\/TSE.2011.55","article-title":"Data mining techniques for software effort estimation: a comparative study","volume":"38","author":"Dejaeger","year":"2012","journal-title":"IEEE Trans Softw Eng"},{"key":"10.1016\/B978-0-12-804206-9.00030-1_bb0020","doi-asserted-by":"crossref","DOI":"10.1145\/2810146.2810152","article-title":"An empirical study of crash-inducing commits in Mozilla Firefox","author":"An","year":"2015"},{"key":"10.1016\/B978-0-12-804206-9.00030-1_bb0025","first-page":"446","article-title":"How to make best use of cross-company data in software effort estimation?","author":"Minku","year":"2014"},{"year":"2007","series-title":"Learning from data streams","author":"Gama","key":"10.1016\/B978-0-12-804206-9.00030-1_bb0030"},{"issue":"4","key":"10.1016\/B978-0-12-804206-9.00030-1_bb0035","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1109\/TKDE.2011.58","article-title":"DDD: a new ensemble approach for dealing with concept drift","volume":"24","author":"Minku","year":"2012","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10.1016\/B978-0-12-804206-9.00030-1_bb0040","first-page":"51","article-title":"Tracking concept drift of software projects using defect prediction quality","author":"Ekanayake","year":"2009"},{"key":"10.1016\/B978-0-12-804206-9.00030-1_bb0045","first-page":"69","article-title":"Can cross-company data improve performance in software effort estimation?","author":"Minku","year":"2012"},{"issue":"1","key":"10.1016\/B978-0-12-804206-9.00030-1_bb0050","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1109\/TR.2014.2370891","article-title":"A learning-to-rank approach to software defect prediction","volume":"64","author":"Yang","year":"2015","journal-title":"IEEE Trans Reliab"},{"key":"10.1016\/B978-0-12-804206-9.00030-1_bb0055","doi-asserted-by":"crossref","DOI":"10.1145\/2499393.2499400","article-title":"Building a second opinion: learning cross-company data","author":"Kocaguneli","year":"2013"},{"issue":"2","key":"10.1016\/B978-0-12-804206-9.00030-1_bb0060","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1109\/TSE.2012.88","article-title":"Active learning and effort estimation: finding the essential content of software effort estimation data","volume":"39","author":"Kocaguneli","year":"2013","journal-title":"IEEE Trans Softw Eng"},{"year":"2006","series-title":"Semi-supervised learning","author":"Chapelle","key":"10.1016\/B978-0-12-804206-9.00030-1_bb0065"},{"issue":"2","key":"10.1016\/B978-0-12-804206-9.00030-1_bb0070","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1109\/TR.2013.2259203","article-title":"Using class imbalance learning for software defect prediction","volume":"62","author":"Wang","year":"2012","journal-title":"IEEE Trans Reliab"},{"key":"10.1016\/B978-0-12-804206-9.00030-1_bb0075","first-page":"382","article-title":"Transfer defect learning","author":"Nam","year":"2013"},{"issue":"5","key":"10.1016\/B978-0-12-804206-9.00030-1_bb0080","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1007\/s10664-008-9103-7","article-title":"On the relative value of cross-company and within-company data for defect prediction","volume":"14","author":"Turhan","year":"2009","journal-title":"Empir Softw Eng"},{"issue":"2","key":"10.1016\/B978-0-12-804206-9.00030-1_bb0085","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1109\/TSE.2011.27","article-title":"Exploiting the essential assumptions of analogy-based effort estimation","volume":"38","author":"Kocaguneli","year":"2012","journal-title":"IEEE Trans Softw Eng"}],"container-title":["Perspectives on Data Science for Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:B9780128042069000301?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:B9780128042069000301?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T04:55:43Z","timestamp":1536555343000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/B9780128042069000301"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9780128042069"],"references-count":16,"URL":"https:\/\/doi.org\/10.1016\/b978-0-12-804206-9.00030-1","relation":{},"subject":[],"published":{"date-parts":[[2016]]}}}