{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007024509,0.0008477076,0.0005686529,0.005076881,0.0004717271,0.0008830489,0.0008647681,0.0008330061,0.002145537],"category_scores_gemma":[0.005144904,0.000172345,0.0007786123,0.002310532,0.0002547821,0.001183205,0.0007327516,0.001016719,0.002097104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000452024,"about_ca_system_score_gemma":0.0008069463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004191707,"about_ca_topic_score_gemma":0.01000855,"domain_scores_codex":[0.9988357,0.0000918834,0.00008809969,0.000370082,0.0004625321,0.0001515528],"domain_scores_gemma":[0.9964407,0.0008350099,0.0006814554,0.0006020905,0.001134897,0.0003058389],"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":[0.0006542101,0.0007176806,0.2057489,0.0003929905,0.0001277904,0.0004374426,0.000324011,0.007185466,0.02103862,0.0007142332,0.02514824,0.7375105],"study_design_scores_gemma":[0.0001036807,0.0007905645,0.374444,0.0002085515,0.0002759606,0.001708167,0.0006483608,0.5425179,0.04473553,0.003084377,0.03136398,0.0001188995],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8382034,0.002292917,0.1212245,0.0006492569,0.0004274638,0.0005476878,0.01205244,0.0182305,0.006371893],"genre_scores_gemma":[0.8992969,0.0003222564,0.07647639,0.00007941429,0.0001062499,0.0001954809,0.01866579,0.0003469799,0.004510485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005076881,"threshold_uncertainty_score":0.008334577,"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."}}