{"id":"W4323851663","doi":"10.1002/smr.2548","title":"Combining object‐oriented metrics and centrality measures to predict faults in object‐oriented software: An empirical validation","year":2023,"lang":"en","type":"article","venue":"Journal of Software Evolution and Process","topic":"Software Engineering Research","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Centrality; Computer science; Software metric; Data mining; Object-oriented programming; Software; Software fault tolerance; Object (grammar); Fault (geology); Artificial intelligence; Software development; Machine learning; Software quality; Programming language; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00229058,0.0002235017,0.0003694928,0.001322654,0.0001935676,0.0001611188,0.0004700953,0.000154677,0.000002748094],"category_scores_gemma":[0.009927225,0.0002088791,0.00005501739,0.004200539,0.00006323346,0.001282065,0.0002068913,0.0005276869,0.000004106178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002656655,"about_ca_system_score_gemma":0.000358428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003729119,"about_ca_topic_score_gemma":0.00001438392,"domain_scores_codex":[0.9969193,0.0002227931,0.0006573937,0.0004580307,0.001221033,0.000521467],"domain_scores_gemma":[0.9972169,0.0008883865,0.0002291078,0.0002666769,0.000893306,0.0005056667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001350851,0.0001497681,0.9762155,0.0001615011,0.00003246962,0.00005584589,0.004102553,0.003740197,0.00006912164,0.00009290987,0.0005016696,0.01474341],"study_design_scores_gemma":[0.001659893,0.0006921825,0.9810665,0.0002732142,0.00001790802,0.0001547318,0.0003582814,0.01322465,0.0004790561,0.001136433,0.0006086117,0.0003285492],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5527773,0.0002977816,0.4458565,0.0002266478,0.0004225716,0.0001687256,0.000006943273,0.0002420122,0.000001532049],"genre_scores_gemma":[0.9826465,0.00008459541,0.01702604,0.00006799062,0.0001094597,0.00001390547,0.000009711224,0.00002118541,0.00002065923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4298692,"threshold_uncertainty_score":0.9984125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03322673717648716,"score_gpt":0.3259724400237865,"score_spread":0.2927457028472993,"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."}}