{"id":"W2059600393","doi":"10.1145/2555596","title":"Predicting Stability of Open-Source Software Systems Using Combination of Bayesian Classifiers","year":2014,"lang":"en","type":"article","venue":"ACM Transactions on Management Information Systems","topic":"Software Engineering Research","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University; Université de Montréal","funders":"","keywords":"Interpretability; Machine learning; Computer science; Software quality; Artificial intelligence; Classifier (UML); Software system; Software; Software evolution; Data mining; Search-based software engineering; Stability (learning theory); Software sizing; Software metric; Component-based software engineering; Context (archaeology); Naive Bayes classifier; Bayesian probability; Software development; Software construction; Support vector machine; Operating system","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":[0.001780113,0.0001491624,0.0002764993,0.0005886735,0.0001648472,0.0003548558,0.001547007,0.00007697924,0.000004300626],"category_scores_gemma":[0.0002247492,0.0001544632,0.00006323317,0.0008602674,0.00004019474,0.00262593,0.00009894893,0.0001491691,0.000008481775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001995439,"about_ca_system_score_gemma":0.0000356186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003484781,"about_ca_topic_score_gemma":0.000001587479,"domain_scores_codex":[0.9977364,0.0001906209,0.0008592405,0.0002002971,0.0007882992,0.0002251204],"domain_scores_gemma":[0.9974308,0.000565005,0.0004231526,0.001182153,0.0003238496,0.00007505908],"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":[0.00003332191,0.0001943361,0.01065484,0.003972272,0.0002092331,3.850007e-7,0.002280253,0.9165859,0.00008053587,0.01522716,0.00007191356,0.05068981],"study_design_scores_gemma":[0.0008207663,0.0001509593,0.002366127,0.0004330438,0.00002379132,0.000004401848,0.00151653,0.9923006,0.0006296836,0.00005773855,0.00151229,0.0001841114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01872757,0.00001088967,0.9786462,0.00003268041,0.0007023608,0.001107213,0.00001238504,0.0002391631,0.0005214975],"genre_scores_gemma":[0.9815117,0.000004792082,0.01831366,0.000009678552,0.000008880802,0.00008437628,0.000009013043,0.00001086061,0.00004701254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9627842,"threshold_uncertainty_score":0.6298827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03265360708425621,"score_gpt":0.2678034671362227,"score_spread":0.2351498600519664,"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."}}