{"id":"W2171172898","doi":"10.1145/1985404.1985407","title":"Extracting code clones for refactoring using combinations of clone metrics","year":2011,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Victoria","keywords":"Code refactoring; clone (Java method); Computer science; Code (set theory); Programming language; Cloning (programming); Computational biology; Biology; Genetics; Software; DNA","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002954593,0.002327166,0.002356918,0.01774259,0.0006593528,0.00189997,0.001193766,0.001279314,0.000790303],"category_scores_gemma":[0.02729485,0.0007143183,0.001753403,0.008386363,0.0004370446,0.002691259,0.001519477,0.0008158513,0.0005386919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006760709,"about_ca_system_score_gemma":0.001485275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002626311,"about_ca_topic_score_gemma":0.005057018,"domain_scores_codex":[0.9940482,0.0009569331,0.001165065,0.001271301,0.002365171,0.0001933003],"domain_scores_gemma":[0.9663928,0.01497224,0.005835404,0.003088527,0.008923226,0.00078779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004517316,0.0004397326,0.1320977,0.001363211,0.0006364145,0.0009666542,0.001288299,0.008465675,0.0671481,0.001611735,0.002421584,0.7831091],"study_design_scores_gemma":[0.0003156842,0.002199551,0.2770175,0.0006557781,0.002226065,0.006353429,0.001757915,0.4904098,0.1816978,0.01292203,0.02374178,0.0007026092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3539187,0.001815101,0.6279698,0.0002392803,0.00006309694,0.001257567,0.002608275,0.009796253,0.002331946],"genre_scores_gemma":[0.3233723,0.0004761154,0.6687028,0.00005559078,0.00003224962,0.0004918393,0.005209737,0.0007524556,0.0009069105],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01774259,"threshold_uncertainty_score":0.01562554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2161381383002295,"score_gpt":0.3510390307316339,"score_spread":0.1349008924314044,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}