{"id":"W4391882875","doi":"10.3390/computers13020052","title":"Interpretable Software Defect Prediction from Project Effort and Static Code Metrics","year":2024,"lang":"en","type":"article","venue":"Computers","topic":"Software Engineering Research","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Fanshawe College","funders":"","keywords":"Interpretability; Computer science; Predictive modelling; Machine learning; Software quality; Reliability (semiconductor); Software; Random forest; Software bug; Artificial intelligence; Data mining; Software metric; Support vector machine; Code (set theory); Source code; Quality (philosophy); Reliability engineering; Software development; Set (abstract data type); Engineering; Programming language","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":[0.004369955,0.0009286763,0.0003811836,0.003650447,0.0001959699,0.001161087,0.0006832432,0.0007200341,0.001114775],"category_scores_gemma":[0.03248987,0.0002248019,0.0007025452,0.002037156,0.000379897,0.001497318,0.000877656,0.0009590084,0.0002447162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008373971,"about_ca_system_score_gemma":0.0007532581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003451201,"about_ca_topic_score_gemma":0.006943066,"domain_scores_codex":[0.9979039,0.0009291078,0.0001752839,0.0003901786,0.0004997276,0.0001018433],"domain_scores_gemma":[0.9658285,0.02267135,0.00510304,0.003286439,0.002854063,0.0002565653],"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":[0.0003376456,0.000431145,0.5368183,0.0003758954,0.0003182231,0.0004149695,0.001324691,0.2577772,0.003096958,0.009029426,0.003421376,0.1866542],"study_design_scores_gemma":[0.00002387862,0.0001979946,0.1193775,0.00008657567,0.0000730315,0.0001326328,0.0004124087,0.8602738,0.002455404,0.01520942,0.001714829,0.00004255091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8065199,0.0002665677,0.1844252,0.0006493734,0.00002728997,0.0001431193,0.004253789,0.001027598,0.002687148],"genre_scores_gemma":[0.9780422,0.00006357295,0.01871048,0.00002078368,0.00000772825,0.00006409601,0.002734374,0.00003216363,0.0003247216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004369955,"threshold_uncertainty_score":0.02311081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01890795632843099,"score_gpt":0.2708190553122697,"score_spread":0.2519110989838387,"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."}}