{"id":"W2883496053","doi":"10.1145/3194095.3194101","title":"Towards a classification of bugs to facilitate software maintainability tasks","year":2018,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Software bug; Computer science; Maintainability; Software maintenance; Process (computing); Software quality; Software; Software regression; Set (abstract data type); Code (set theory); Software engineering; Software development; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.0006871516,0.00009717013,0.0001278752,0.0001544504,0.00004843974,0.00004756211,0.0009268795,0.00004895986,0.00007507554],"category_scores_gemma":[0.002496118,0.00008686745,0.00004483186,0.0007248076,0.0001007135,0.0002053621,0.0003788318,0.00008593407,0.0002233011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001334494,"about_ca_system_score_gemma":0.0001476027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002138617,"about_ca_topic_score_gemma":0.00001798703,"domain_scores_codex":[0.9986429,0.00004757613,0.0002116527,0.0003876295,0.0004065782,0.000303642],"domain_scores_gemma":[0.9980596,0.0002819955,0.00002588483,0.0009703668,0.0005074354,0.0001547736],"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":[0.0000737119,0.0003477303,0.0959422,0.0003202473,0.00006420028,0.00001058099,0.01133382,0.0002997909,0.01541799,0.093057,0.0187861,0.7643466],"study_design_scores_gemma":[0.000244276,0.0006154228,0.9498227,0.00002258971,0.00000235003,0.00000550236,0.0001025241,0.01119872,0.01864903,0.008374473,0.01067136,0.000291017],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1902421,0.000005480609,0.8076262,0.0009590514,0.0001214506,0.0002015873,0.000004000162,0.0003336032,0.000506493],"genre_scores_gemma":[0.8047202,3.652178e-7,0.194669,0.00006641987,0.00002726888,0.00002837141,8.944286e-7,0.000005476399,0.0004820068],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8538805,"threshold_uncertainty_score":0.3542352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0572385695500097,"score_gpt":0.3036218594995165,"score_spread":0.2463832899495068,"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."}}