{"id":"W2029277954","doi":"10.1007/s11219-014-9230-x","title":"Predicting defective modules in different test phases","year":2014,"lang":"en","type":"article","venue":"Software Quality Journal","topic":"Software Engineering Research","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Reliability engineering; Computer science; Predictive modelling; Software; Data mining; Machine learning; Engineering; Programming language","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":[0.001644843,0.0009835205,0.0005692478,0.004450014,0.0002589905,0.0008867672,0.001089194,0.001229107,0.001660301],"category_scores_gemma":[0.01782574,0.0003915818,0.001037651,0.00142937,0.0003652509,0.0009333855,0.0004956094,0.0006376907,0.000651984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005506662,"about_ca_system_score_gemma":0.0005966031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005593194,"about_ca_topic_score_gemma":0.006924803,"domain_scores_codex":[0.9987613,0.0002043893,0.0001329952,0.0002691586,0.0004269469,0.0002052227],"domain_scores_gemma":[0.9723652,0.01561128,0.004287153,0.00153866,0.004799532,0.001398198],"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":[0.00116196,0.0004972607,0.8908943,0.00009911325,0.000165032,0.0003423811,0.0001057031,0.03031568,0.01134613,0.0002023673,0.0007930036,0.06407714],"study_design_scores_gemma":[0.00006016117,0.001846646,0.5972244,0.0000493361,0.0003767913,0.001106547,0.0002822672,0.3730693,0.02407999,0.00115199,0.0006991134,0.00005338943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915222,0.000134896,0.006933991,0.00003560775,0.00001244468,0.00001735636,0.0004883252,0.0005694918,0.0002858331],"genre_scores_gemma":[0.9941676,0.00003183902,0.004140844,0.00001482747,0.000004826716,0.000006785825,0.001164686,0.00004750365,0.0004211254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005593194,"threshold_uncertainty_score":0.01112127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02811477016643656,"score_gpt":0.3126412137085472,"score_spread":0.2845264435421107,"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."}}