{"id":"W2783794442","doi":"10.1016/j.infsof.2018.01.003","title":"Support vector regression for predicting software enhancement effort","year":2018,"lang":"en","type":"article","venue":"Information and Software Technology","topic":"Software Engineering Research","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"Consejo Nacional de Ciencia y Tecnología; National Research Council Canada; Universidad de Guadalajara","keywords":"Support vector machine; Computer science; Benchmarking; Machine learning; Artificial neural network; Data mining; Software; Sigmoid function; Artificial intelligence; Decision tree; Set (abstract data type); Regression analysis; Kernel (algebra); Radial basis function kernel; Kernel method; Mathematics; Operating system","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002530915,0.000867845,0.0005176873,0.001597512,0.0001567205,0.0004892022,0.0006544951,0.0006786282,0.001499709],"category_scores_gemma":[0.0172024,0.0002013871,0.0004200539,0.001418291,0.0001547625,0.0009608695,0.0003439657,0.001162363,0.00085351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002709794,"about_ca_system_score_gemma":0.0005301769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00416774,"about_ca_topic_score_gemma":0.003371783,"domain_scores_codex":[0.9988426,0.0005660662,0.0000657928,0.0001541916,0.0002848288,0.00008640238],"domain_scores_gemma":[0.9859154,0.01106836,0.0009189697,0.0006540962,0.001214777,0.0002283226],"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":[0.001340179,0.00178662,0.1304577,0.0002196018,0.0002947373,0.0001273497,0.0001439978,0.2416735,0.006893213,0.001358679,0.004258111,0.6114463],"study_design_scores_gemma":[0.00001814937,0.0002365601,0.01317083,0.00001200141,0.00003437891,0.00001994989,0.00003188555,0.9838015,0.00175663,0.0006849268,0.0002229474,0.00001028644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.847149,0.0007144175,0.1487269,0.0002242223,0.00007475007,0.00005640989,0.0006561537,0.001363513,0.001034711],"genre_scores_gemma":[0.9621798,0.0001770901,0.035083,0.0000166858,0.00002977228,0.00003701137,0.0007338025,0.0000436118,0.001699169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00416774,"threshold_uncertainty_score":0.01338494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0107729103410717,"score_gpt":0.2680281242044023,"score_spread":0.2572552138633306,"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."}}