{"id":"W1497968089","doi":"10.1023/a:1024424811345","title":"Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques","year":2003,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Software Reliability and Analysis Research","field":"Computer Science","cited_by":128,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"McGill University","keywords":"Computer science; Software quality; Software; Artificial neural network; Approximation error; Reliability engineering; Data mining; Artificial intelligence; Machine learning; Algorithm; Software development; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009127711,0.001388179,0.001914033,0.006072673,0.0005506693,0.001360064,0.002671942,0.001518341,0.001093708],"category_scores_gemma":[0.04170169,0.000462617,0.001695392,0.004516779,0.0005509867,0.003461767,0.001352356,0.001327667,0.0003598735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001167705,"about_ca_system_score_gemma":0.001221229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01091064,"about_ca_topic_score_gemma":0.007657822,"domain_scores_codex":[0.9945322,0.002550719,0.0004172797,0.0005544063,0.001724745,0.0002204871],"domain_scores_gemma":[0.9385799,0.05106867,0.00302878,0.002796893,0.004254357,0.0002713654],"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":[0.001286863,0.0005207744,0.02582925,0.000956558,0.001152456,0.00005050145,0.0003388249,0.3516938,0.0008224197,0.005231755,0.001555847,0.6105609],"study_design_scores_gemma":[0.00007871245,0.0004765113,0.008125781,0.0001473865,0.0003856381,0.00008432059,0.0001605712,0.9793963,0.000921906,0.009486163,0.0006949401,0.00004175597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2186677,0.01265556,0.7622088,0.0009445917,0.0001371771,0.0001533787,0.0006193991,0.002165653,0.002447799],"genre_scores_gemma":[0.8536903,0.006941518,0.137444,0.0001367704,0.0001548182,0.0001680449,0.0007252484,0.0001933283,0.0005460717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01091064,"threshold_uncertainty_score":0.04827249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07721747522635733,"score_gpt":0.3501777772525694,"score_spread":0.2729603020262121,"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."}}