{"id":"W2166414580","doi":"10.1109/msr.2007.7","title":"Determining Implementation Expertise from Bug Reports","year":2007,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":110,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Software engineering; Software bug; False positive paradox; Software; Software maintenance; Set (abstract data type); Product (mathematics); Code review; Software peer review; Data science; Software development; Static program analysis; Software construction; Programming language; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02145263,0.001134728,0.001574506,0.02885595,0.001001455,0.002621318,0.001410608,0.001942141,0.001267709],"category_scores_gemma":[0.1948619,0.0007219418,0.001136824,0.007938565,0.0007912993,0.004741967,0.003395245,0.001603407,0.001143468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009016074,"about_ca_system_score_gemma":0.001423359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004879403,"about_ca_topic_score_gemma":0.006487314,"domain_scores_codex":[0.967173,0.008375038,0.004056154,0.004693242,0.01449621,0.001206431],"domain_scores_gemma":[0.6933117,0.1878275,0.03869571,0.02436113,0.05308764,0.002716402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004583575,0.0004103965,0.4650068,0.001681035,0.0005507316,0.0006555779,0.00776285,0.007758606,0.008191017,0.001263711,0.009344291,0.4969165],"study_design_scores_gemma":[0.0001952262,0.0009171458,0.8086644,0.0008723406,0.0009286721,0.003600297,0.005607718,0.09787505,0.03930156,0.007697134,0.0338564,0.0004841082],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8270993,0.002162448,0.1499338,0.0005618709,0.0001123768,0.0009824125,0.006885168,0.002285979,0.009976715],"genre_scores_gemma":[0.8745543,0.0007579376,0.109469,0.0001302485,0.0001114625,0.0005187369,0.01212533,0.0002219504,0.002111076],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02885595,"threshold_uncertainty_score":0.1134537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02233596572363169,"score_gpt":0.3348464797874061,"score_spread":0.3125105140637744,"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."}}