{"id":"W2046347832","doi":"10.1145/2593801.2593803","title":"A mapping study on bayesian networks for software quality prediction","year":2014,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Tekes","keywords":"Bayesian network; Computer science; Categorical variable; Machine learning; Artificial intelligence; Dependency (UML); Inference; Data mining; Software; Quality (philosophy); Software quality; Software development","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":[0.001387034,0.0001043778,0.0001285633,0.0001155031,0.0001177615,0.0001254969,0.0005467745,0.00005028615,0.000004894473],"category_scores_gemma":[0.001266663,0.00009326558,0.00004774303,0.0003165337,0.000009801276,0.0001594221,0.0001341944,0.0001376724,0.00001154443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005399405,"about_ca_system_score_gemma":0.00001786524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002082112,"about_ca_topic_score_gemma":0.000005287196,"domain_scores_codex":[0.9986928,0.00009929229,0.0001924487,0.0003929675,0.0003184795,0.0003040715],"domain_scores_gemma":[0.9975058,0.001644029,0.00002968079,0.0006442866,0.00008174604,0.00009440872],"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":[0.00004642988,0.0008913832,0.5783672,0.000117922,0.0001240843,0.000004455159,0.003249381,0.07028735,0.00006065231,0.01993936,0.009092689,0.3178191],"study_design_scores_gemma":[0.0005490992,0.0005283541,0.3965684,0.00001709478,0.000001321262,9.73801e-7,0.00006147751,0.6005449,0.00002920578,0.0005769592,0.0009803863,0.0001417562],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02263925,0.000004777304,0.9752053,0.000143724,0.0004016573,0.0005339002,9.805939e-7,0.0009601038,0.0001102952],"genre_scores_gemma":[0.9393881,2.770084e-7,0.05998056,0.0001042719,0.0001789674,0.0001287911,0.000001998232,0.00001192909,0.0002050877],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9167489,"threshold_uncertainty_score":0.380326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03577368909895908,"score_gpt":0.3062866284295387,"score_spread":0.2705129393305797,"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."}}