{"id":"W1978859404","doi":"10.1109/ase.2013.6693087","title":"Personalized defect prediction","year":2013,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":243,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Software bug; Java; Commit; Eclipse; Source lines of code; Python (programming language); Software; Code (set theory); Software evolution; Machine learning; Predictive modelling; Kernel (algebra); Linux kernel; Data mining; Artificial intelligence; Programming language; Operating system; Software development; Database; Software construction","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":[0.001096561,0.001379062,0.001061283,0.004155368,0.0002658584,0.0008229433,0.001544872,0.001131902,0.001906557],"category_scores_gemma":[0.007566814,0.0004255445,0.0009114549,0.001964442,0.0002183657,0.001665868,0.0008255977,0.001000195,0.001506359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004584842,"about_ca_system_score_gemma":0.0005389292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005104979,"about_ca_topic_score_gemma":0.007841356,"domain_scores_codex":[0.998365,0.0001714144,0.00009122947,0.0006004607,0.0006161558,0.0001557255],"domain_scores_gemma":[0.9914011,0.003220923,0.001306554,0.001798423,0.001986161,0.0002867291],"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":[0.0003849339,0.000668414,0.2792992,0.0002719755,0.0002786104,0.0005209224,0.0001928818,0.08230295,0.007927264,0.0009231439,0.02517552,0.6020542],"study_design_scores_gemma":[0.00003725139,0.0003454543,0.06212856,0.00004263276,0.0001652979,0.0007553306,0.0001187854,0.9140806,0.01165076,0.003764558,0.00683693,0.00007388258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4657866,0.001698893,0.4840877,0.001113134,0.0002365203,0.000410864,0.01138532,0.02903387,0.006247004],"genre_scores_gemma":[0.8789325,0.0004931695,0.1011937,0.0002261895,0.0001156784,0.0001955574,0.01257168,0.0006004574,0.005671102],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005104979,"threshold_uncertainty_score":0.01015055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01363244648046836,"score_gpt":0.2366302322925787,"score_spread":0.2229977858121104,"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."}}