{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001008817,0.00004068131,0.0000377051,0.00005323103,0.00002803639,0.000104692,0.0003013711,0.00002068154,0.0003834018],"category_scores_gemma":[0.000159231,0.00003316678,0.00003341935,0.0001833671,0.00001192178,0.0003317056,0.00008178296,0.00005782201,0.001093224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000234425,"about_ca_system_score_gemma":0.00001599086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005146565,"about_ca_topic_score_gemma":3.071542e-7,"domain_scores_codex":[0.9994611,0.00001383942,0.00004922561,0.0001418338,0.0001864095,0.000147653],"domain_scores_gemma":[0.9994754,0.0001717018,0.000005126351,0.0002388637,0.00004782449,0.00006112951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000004674479,0.0001801319,0.2325314,0.00008119529,0.0001426746,0.00002278842,0.002049517,0.0007402925,0.01666556,0.1984154,0.3037671,0.2453994],"study_design_scores_gemma":[0.0007428276,0.0001688937,0.5412211,0.00001651311,0.000003046814,0.0000473303,0.0000248027,0.4302142,0.003781612,0.003884878,0.01959177,0.0003030823],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07291514,0.00004763866,0.9227338,0.0003941922,0.0001652809,0.0001187353,1.836182e-7,0.0007765654,0.002848399],"genre_scores_gemma":[0.9516549,0.000002921072,0.04459907,0.00008209313,0.00003986671,0.00004837754,5.333723e-7,0.00000476478,0.003567507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8787397,"threshold_uncertainty_score":0.9996845,"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."}}