{"id":"W2038917214","doi":"10.1016/j.infsof.2014.12.006","title":"ELBlocker: Predicting blocking bugs with ensemble imbalance learning","year":2015,"lang":"en","type":"article","venue":"Information and Software Technology","topic":"Software Engineering Research","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"National Key Research and Development Program of China","keywords":"Blocking (statistics); Computer science; Software bug; Disjoint sets; Eclipse; Artificial intelligence; Software; Programming language; Mathematics; Computer network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003286245,0.0001137371,0.0001280222,0.0004185116,0.0001296432,0.0001486515,0.0003999818,0.0001317487,0.000001343374],"category_scores_gemma":[0.00154619,0.0001014011,0.00001118779,0.0007037995,0.0000679868,0.001078501,0.0002887325,0.0003781738,0.00003785595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005546529,"about_ca_system_score_gemma":0.00009754845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001381842,"about_ca_topic_score_gemma":0.000002616403,"domain_scores_codex":[0.9990509,0.0000138192,0.0001987545,0.000151077,0.0002829553,0.0003024797],"domain_scores_gemma":[0.9991408,0.0001524463,0.00008799513,0.0002874302,0.0002328142,0.00009848845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001134719,0.00001084462,0.7089379,0.00004690965,0.00002075531,0.00001549238,0.002449736,0.003483993,0.00002604776,0.005293842,0.0003647729,0.2793384],"study_design_scores_gemma":[0.009624386,0.003706868,0.08885973,0.0007956499,0.00003785725,0.004519556,0.004811702,0.5917366,0.01260604,0.01328598,0.2670554,0.002960263],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2940861,0.0001281973,0.702217,0.0005078371,0.00007970952,0.0001071284,4.202919e-7,0.002682002,0.0001915518],"genre_scores_gemma":[0.9144351,0.00001224355,0.08538594,0.00007143107,0.00001433528,0.00002524381,0.000003174574,0.000006760365,0.00004575616],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.620349,"threshold_uncertainty_score":0.4135015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008737206462084155,"score_gpt":0.2217211099907938,"score_spread":0.2129839035287096,"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."}}