{"paper":{"title":"Revisiting Unsupervised Learning for Defect Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SE","authors_text":"Tim Menzies, Wei Fu","submitted_at":"2017-03-01T04:36:06Z","abstract_excerpt":"Collecting quality data from software projects can be time-consuming and expensive. Hence, some researchers explore \"unsupervised\" approaches to quality prediction that does not require labelled data. An alternate technique is to use \"supervised\" approaches that learn models from project data labelled with, say, \"defective\" or \"not-defective\". Most researchers use these supervised models since, it is argued, they can exploit more knowledge of the projects.\n  At FSE'16, Yang et al. reported startling results where unsupervised defect predictors outperformed supervised predictors for effort-awar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.00132","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}