{"id":"W4402315368","doi":"10.1016/j.jss.2024.112205","title":"Feature transformation for improved software bug detection and commit classification","year":2024,"lang":"en","type":"article","venue":"Journal of Systems and Software","topic":"Software Engineering Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Commit; Transformation (genetics); Computer science; Feature (linguistics); Software bug; Software; Pattern recognition (psychology); Artificial intelligence; Data mining; Programming language; Database","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006731058,0.00010851,0.0001857724,0.0002351751,0.0001188008,0.0005392491,0.0001728119,0.000115875,3.255032e-7],"category_scores_gemma":[0.0003300544,0.00008577039,0.00006346469,0.0002087925,0.00001972702,0.0008510952,0.00002274922,0.0002526665,7.322424e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007544662,"about_ca_system_score_gemma":0.00006181414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007153221,"about_ca_topic_score_gemma":0.000002820254,"domain_scores_codex":[0.9991952,0.00003262798,0.000260295,0.0001562358,0.000202796,0.0001528827],"domain_scores_gemma":[0.9988338,0.0005688703,0.00009641216,0.0001359827,0.0002603415,0.0001045879],"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":[0.00008962303,0.00004517621,0.003340261,0.004987205,0.000248011,0.00002675798,0.004471857,0.0003854839,0.009949573,0.00278898,0.004217216,0.9694499],"study_design_scores_gemma":[0.003168855,0.002330276,0.07144649,0.003204603,0.000164697,0.004138925,0.001035076,0.6970276,0.001929704,0.002646099,0.2118682,0.001039447],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02934271,0.006500619,0.9619384,0.0008193249,0.0009528565,0.0002836141,0.00001105552,0.0001498187,0.000001587246],"genre_scores_gemma":[0.9787934,0.0002254956,0.02053027,0.0000162389,0.0002651451,0.00002523517,0.000002403729,0.00001787189,0.0001239219],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9684104,"threshold_uncertainty_score":0.5199991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01854535350249481,"score_gpt":0.260884561329171,"score_spread":0.2423392078266762,"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."}}