{"id":"W4379930888","doi":"10.1109/iciccs56967.2023.10142534","title":"Explainable Software Defect Prediction from Cross Company Project Metrics using Machine Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Fanshawe College","funders":"","keywords":"Computer science; Software bug; Schedule; Machine learning; Sizing; Software; Predictive modelling; Product metric; Transparency (behavior); Software metric; Artificial intelligence; Class (philosophy); Data mining; Software engineering; Software development; Software quality","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":[0.0008244184,0.0001750598,0.0001922498,0.0007269164,0.0003543891,0.0004884867,0.0007481504,0.00009170238,0.00003024547],"category_scores_gemma":[0.002688714,0.000166862,0.00009917156,0.003897578,0.00003047218,0.000679749,0.000686137,0.0004065525,0.0001554338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001977365,"about_ca_system_score_gemma":0.0001221264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001928978,"about_ca_topic_score_gemma":0.00001355078,"domain_scores_codex":[0.9978554,0.0001069375,0.000228793,0.0005478044,0.0006762312,0.000584817],"domain_scores_gemma":[0.9975782,0.001614668,0.00004854154,0.0005087241,0.0001471452,0.0001027241],"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":[0.000009072113,0.00003700334,0.8634899,0.00005812577,0.00006636533,0.00009178422,0.000684902,0.1277929,0.0008800472,0.000174464,0.0009377707,0.005777684],"study_design_scores_gemma":[0.0003161032,0.00006563268,0.05741851,0.00002366551,0.000005436569,0.00001068775,0.00002928416,0.9383386,0.001554044,0.0001327899,0.001920811,0.0001844568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3992051,0.0001429836,0.5972095,0.00001404203,0.0003316644,0.0001682567,0.00001140868,0.002877085,0.00003998332],"genre_scores_gemma":[0.8450749,0.00002798619,0.1532568,0.00002119722,0.000141972,0.00003835145,0.00006654182,0.00004882506,0.001323481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8105457,"threshold_uncertainty_score":0.6804433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05780434819787134,"score_gpt":0.3175388171236185,"score_spread":0.2597344689257471,"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."}}