{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00140083,0.0002906085,0.0004805184,0.0002253725,0.0003116049,0.0002764199,0.0006873557,0.0002012792,0.000008864174],"category_scores_gemma":[0.005380118,0.000292858,0.0002656979,0.0008240814,0.00003255575,0.0006636013,0.0001687635,0.0004158302,0.00001126196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002562927,"about_ca_system_score_gemma":0.0001121793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002957987,"about_ca_topic_score_gemma":0.000004850769,"domain_scores_codex":[0.997311,0.000130758,0.0006674411,0.0006625305,0.000638091,0.0005901718],"domain_scores_gemma":[0.9972937,0.001359376,0.0000840328,0.0007361227,0.0002821524,0.0002446934],"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":[0.00001064787,0.0001672034,0.09468009,0.0003084925,0.00008055412,0.000003888579,0.0003951948,0.8871707,0.00007651346,0.002342355,0.0003664099,0.01439798],"study_design_scores_gemma":[0.0003061675,0.00006204323,0.001832844,0.00007526464,0.00001441691,0.000008300578,0.00001089933,0.9912236,0.001157902,0.002270302,0.002696077,0.0003421484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05314642,0.0001534791,0.9435468,0.0001609792,0.0001758099,0.0004006998,0.00001065437,0.002389677,0.00001547486],"genre_scores_gemma":[0.4940997,0.000005026055,0.5055825,0.00005551093,0.00005706008,0.0001340235,0.00002253186,0.0000240018,0.00001971609],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4409533,"threshold_uncertainty_score":0.9999524,"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."}}