{"id":"W2024209326","doi":"10.1016/j.ins.2005.12.002","title":"Identification of defect-prone classes in telecommunication software systems using design metrics","year":2005,"lang":"en","type":"article","venue":"Information Sciences","topic":"Software Engineering Research","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Negative binomial distribution; Computer science; Suite; Poisson regression; Software; Regression analysis; Data mining; Identification (biology); Poisson distribution; Statistics; Machine learning; Mathematics; Programming language; Population","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":[0.002970476,0.00006962402,0.0001047833,0.00103133,0.0001188682,0.0003615332,0.001156047,0.00004655523,0.000001461402],"category_scores_gemma":[0.002130383,0.00006579039,0.0000248178,0.003070174,0.00008332183,0.005296769,0.0001358475,0.00008994631,0.00002908762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001565819,"about_ca_system_score_gemma":0.0001960858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008042093,"about_ca_topic_score_gemma":0.000003740916,"domain_scores_codex":[0.9983827,0.00009808057,0.0005658835,0.0001181098,0.0006463928,0.0001888207],"domain_scores_gemma":[0.9981752,0.0008824953,0.0002671523,0.0003836828,0.0002571325,0.00003432767],"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":[0.000001608632,0.00002591885,0.008591964,0.00004834365,0.000003748855,9.784177e-8,0.001331915,0.9370441,0.001102571,0.002817103,0.0000540904,0.04897853],"study_design_scores_gemma":[0.00009938719,0.00002735937,0.01128245,0.0000296757,0.000001278725,0.000005720668,0.0001235855,0.982079,0.005906098,0.00009117748,0.0002687257,0.00008551995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09857351,0.0002413101,0.9006229,0.00006945535,0.0001230231,0.0002299068,0.00000102082,0.00009399081,0.00004487073],"genre_scores_gemma":[0.8612731,0.00002250855,0.1386591,0.00001235406,0.000009014777,0.00001669148,0.00000118264,0.000001739342,0.000004297826],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7626996,"threshold_uncertainty_score":0.384003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07743519655449478,"score_gpt":0.3272002163318417,"score_spread":0.2497650197773469,"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."}}