{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001985799,0.00102944,0.001108537,0.002132852,0.0004174755,0.0008649399,0.001417413,0.0009076418,0.00110077],"category_scores_gemma":[0.009296235,0.0004159092,0.001466084,0.001611676,0.0003728829,0.001448365,0.0006981559,0.001756635,0.0008906883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008450408,"about_ca_system_score_gemma":0.001039493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01000155,"about_ca_topic_score_gemma":0.006936447,"domain_scores_codex":[0.9988166,0.0003437588,0.0001032541,0.0003189903,0.000310604,0.0001067773],"domain_scores_gemma":[0.9962273,0.002056722,0.0003334355,0.0004233898,0.0008979085,0.00006125636],"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":[0.0002203881,0.0003044877,0.01208752,0.00006422828,0.0001152106,0.0001327516,0.00008648897,0.6792794,0.003952596,0.001895703,0.002302644,0.2995585],"study_design_scores_gemma":[0.000004707338,0.00002081885,0.0004336541,0.000002997295,0.000007151023,0.00001835404,0.000004815092,0.9976624,0.0004838223,0.001193113,0.0001639714,0.000004238865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1125627,0.0003511039,0.8791732,0.000318875,0.0000628428,0.000103987,0.0005726169,0.006138475,0.0007161699],"genre_scores_gemma":[0.780372,0.0001576536,0.2157671,0.00009499738,0.00004408761,0.0001820614,0.002153433,0.0002312671,0.0009973054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01000155,"threshold_uncertainty_score":0.01988667,"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."}}