{"id":"W3008252796","doi":"10.1109/icmla.2019.00096","title":"Feature Changes in Source Code for Commit Classification Into Maintenance Activities","year":2019,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Commit; Software maintenance; Computer science; Boosting (machine learning); Backporting; Metric (unit); Software development; Software engineering; Feature (linguistics); Software; Baseline (sea); Software bug; Machine learning; Data mining; Artificial intelligence; Software construction; Database; Engineering; 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":[0.0002645809,0.00007887324,0.000101338,0.0001281416,0.00002847285,0.00008323841,0.0005772763,0.00006135127,0.00001144007],"category_scores_gemma":[0.0001597449,0.00006943835,0.0000213688,0.0002498917,0.00001802389,0.0001974781,0.0001202748,0.0001391179,0.0000393256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001004339,"about_ca_system_score_gemma":0.00002933593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002043396,"about_ca_topic_score_gemma":0.0002971153,"domain_scores_codex":[0.9992658,0.00001921092,0.00005300254,0.0002517202,0.0001645481,0.000245669],"domain_scores_gemma":[0.9988396,0.0006378017,0.00002198677,0.0004213827,0.00004311357,0.00003608618],"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":[0.0001196593,0.0002635011,0.2566911,0.0009218068,0.00006178977,0.000008134149,0.01083589,0.003202405,0.06362843,0.1907157,0.1164549,0.3570967],"study_design_scores_gemma":[0.001219415,0.0002637474,0.1174848,0.0001401398,0.000001869051,0.000008186972,0.00072875,0.5640904,0.02005025,0.001633123,0.2938566,0.0005227355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1101506,0.00005421055,0.8756696,0.01280773,0.0002168365,0.0004520646,0.000002477234,0.0002950386,0.0003513867],"genre_scores_gemma":[0.9404998,0.000008541675,0.04534314,0.0001922726,0.00003536449,0.0001094092,0.000002953646,0.00001219858,0.0137963],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8303492,"threshold_uncertainty_score":0.2831614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02252568773051401,"score_gpt":0.2739560130404134,"score_spread":0.2514303253098994,"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."}}