{"id":"W4293228314","doi":"10.1145/3511430.3511444","title":"Feature Transformation for Improved Software Bug Detection Models","year":2022,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Software regression; Software bug; Machine learning; Artificial intelligence; Software; Classifier (UML); Random forest; Transformation (genetics); Precision and recall; Data mining; Feature selection; Feature (linguistics); Predictive modelling; Software quality; Data transformation; Software development; Programming language","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":[0.0002746043,0.00007307667,0.00006672248,0.0001217274,0.0002534798,0.0000763007,0.0004553948,0.00003148857,0.0000108246],"category_scores_gemma":[0.00007158175,0.00007297176,0.00005706731,0.0003502298,0.00000487942,0.0005700465,0.0001034636,0.0001764188,0.000002519683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001260788,"about_ca_system_score_gemma":0.0000420891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001187365,"about_ca_topic_score_gemma":0.000005205938,"domain_scores_codex":[0.9992736,0.00001933528,0.00007507255,0.0002018732,0.000211523,0.0002185751],"domain_scores_gemma":[0.9994205,0.0001937517,0.00001684534,0.0002556247,0.00006624646,0.00004698977],"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":[0.00008270369,0.0001048039,0.00004467882,0.0001434138,0.00004862341,0.000002330876,0.00281298,0.2268627,0.01604904,0.01514273,0.007115759,0.7315902],"study_design_scores_gemma":[0.0003292633,0.0001491154,0.00009847049,0.00000111396,0.000001619744,0.00001247861,0.00002498267,0.9802975,0.006343687,0.00369934,0.008930421,0.0001120091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002767691,0.00003494437,0.9944097,0.001245712,0.0003203582,0.000436785,0.000009410216,0.0007282011,0.00004715179],"genre_scores_gemma":[0.8517259,0.000002267062,0.1463921,0.0001690784,0.00004250113,0.0005971053,0.00001416816,0.00001674137,0.001040084],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8489583,"threshold_uncertainty_score":0.2975702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01609412741448337,"score_gpt":0.2398867386999696,"score_spread":0.2237926112854862,"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."}}