{"id":"W2089863833","doi":"10.1109/seaa.2011.59","title":"Empirical Evaluation of Mixed-Project Defect Prediction Models","year":2011,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Metric (unit); Predictive modelling; Product metric; Data modeling; Data mining; Focus (optics); Project management; Data collection; Software bug; Empirical research; Data science; Machine learning; Database; Software; Engineering; Systems engineering; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02755327,0.002420178,0.00181157,0.002813723,0.0006277511,0.00214836,0.003448514,0.001963799,0.001317923],"category_scores_gemma":[0.04823234,0.0007927321,0.001234884,0.001942731,0.0009290242,0.003566249,0.001920574,0.002215263,0.0008474067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001456997,"about_ca_system_score_gemma":0.001101591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008397335,"about_ca_topic_score_gemma":0.006264572,"domain_scores_codex":[0.9904614,0.005835665,0.0006972085,0.001663833,0.001022651,0.0003192087],"domain_scores_gemma":[0.9065637,0.07468996,0.003612646,0.006899242,0.006491937,0.001742424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001952134,0.001255102,0.1595243,0.0003361718,0.001011816,0.0002182623,0.0002608914,0.7468019,0.0008961053,0.001033757,0.004418963,0.08229066],"study_design_scores_gemma":[0.00003771821,0.0002595836,0.005508624,0.00001715973,0.00003879708,0.00004619952,0.00004843696,0.9928939,0.0003814457,0.0005631766,0.0001895724,0.00001534459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9520673,0.001422981,0.04092997,0.0005728811,0.00007773569,0.0001136051,0.002090964,0.001421475,0.001303038],"genre_scores_gemma":[0.9751549,0.0001775324,0.01998665,0.00005925474,0.00003466661,0.00009840259,0.003972834,0.00007983352,0.0004359675],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02755327,"threshold_uncertainty_score":0.1457173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.232463589436113,"score_gpt":0.3634117234265972,"score_spread":0.1309481339904842,"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."}}