{"id":"W4379408297","doi":"10.2139/ssrn.4469912","title":"Commit-Time Defect Prediction Using One-Class Classification","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Commit; Class (philosophy); Computer science; Artificial intelligence; Machine learning; Database","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":[0.001003701,0.001063545,0.001532273,0.002099921,0.0004537135,0.001141642,0.001782539,0.001367022,0.001715178],"category_scores_gemma":[0.004574516,0.0002586734,0.0006752168,0.001220125,0.0003227622,0.001170512,0.0008675705,0.001231436,0.00101118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004523191,"about_ca_system_score_gemma":0.0008753368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004893584,"about_ca_topic_score_gemma":0.005476965,"domain_scores_codex":[0.9989923,0.00008487413,0.00007493209,0.0003128752,0.0003433025,0.0001917365],"domain_scores_gemma":[0.9935819,0.002536683,0.0006899054,0.001018436,0.001809157,0.00036402],"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":[0.001933946,0.001175139,0.04752215,0.0002169735,0.0001446344,0.0005131559,0.0001228196,0.09097118,0.01919466,0.001252786,0.008172054,0.8287805],"study_design_scores_gemma":[0.00001365116,0.0001406414,0.005233495,0.00000952367,0.00002425767,0.0001268454,0.00003175289,0.9884083,0.004602295,0.0009908313,0.0004052442,0.00001318983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5698305,0.001255026,0.4178698,0.0004675783,0.000508966,0.0001767465,0.001370942,0.00539197,0.003128574],"genre_scores_gemma":[0.9528979,0.0001491902,0.04242374,0.0000535612,0.0001019783,0.00006224476,0.001352615,0.0001073017,0.002851433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004893584,"threshold_uncertainty_score":0.00973016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05915975244909197,"score_gpt":0.2634481417183164,"score_spread":0.2042883892692244,"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."}}