{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005258295,0.00144111,0.001491504,0.005781219,0.0008027423,0.00232044,0.001299223,0.002076322,0.0007787069],"category_scores_gemma":[0.01724196,0.0006775244,0.001538477,0.002059748,0.0004859637,0.002698902,0.001310831,0.001759643,0.0006359023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001333966,"about_ca_system_score_gemma":0.001058444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006540008,"about_ca_topic_score_gemma":0.006999184,"domain_scores_codex":[0.9964578,0.0009401609,0.0003204348,0.0006854777,0.001314032,0.0002821238],"domain_scores_gemma":[0.9876001,0.007315063,0.001384587,0.0006069697,0.002663428,0.0004297866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005603141,0.0005473993,0.09800532,0.0001823667,0.0005549437,0.0002229374,0.0003732683,0.421396,0.006726931,0.002491849,0.002065856,0.4668728],"study_design_scores_gemma":[0.000006115048,0.00006185039,0.005412749,0.00001691007,0.00006362129,0.00003421643,0.00003491749,0.9903093,0.001329625,0.002402217,0.0003109661,0.00001761608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3024751,0.001138543,0.691578,0.0004313475,0.00008969026,0.0001918931,0.0003578071,0.001222356,0.002515204],"genre_scores_gemma":[0.8843839,0.0002983665,0.1133792,0.00008261865,0.00008938618,0.00009535421,0.0007451762,0.00005921208,0.0008668444],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006540008,"threshold_uncertainty_score":0.02780885,"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."}}