{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002364532,0.0007703314,0.0004270471,0.002334902,0.0001870527,0.0007933616,0.0007759646,0.0006380748,0.0009324156],"category_scores_gemma":[0.01203056,0.0002276566,0.0006712379,0.001339327,0.0002554834,0.001418235,0.0006327752,0.0009106632,0.0001406727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008241765,"about_ca_system_score_gemma":0.0007948652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003858197,"about_ca_topic_score_gemma":0.006936627,"domain_scores_codex":[0.9992066,0.0003273492,0.00005205061,0.0001876874,0.0001612185,0.00006515665],"domain_scores_gemma":[0.9850797,0.01162752,0.001284448,0.0009806005,0.0008713474,0.0001563287],"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":[0.0001310218,0.0002477928,0.08281898,0.0001191095,0.0002069828,0.0001873464,0.0002050073,0.7739961,0.0008548318,0.007775839,0.001370839,0.1320861],"study_design_scores_gemma":[0.000004532907,0.00003166413,0.004672955,0.000007850446,0.00001335194,0.0000163304,0.00001747612,0.9897172,0.0002763314,0.005062675,0.0001740091,0.000005522773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5595739,0.000420096,0.4359815,0.0004795205,0.00002693343,0.0000831425,0.001132998,0.0008422246,0.001459612],"genre_scores_gemma":[0.9695475,0.0001077877,0.02883651,0.00001982838,0.00001412751,0.00004400668,0.001020455,0.00002271333,0.0003871789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003858197,"threshold_uncertainty_score":0.01250499,"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."}}