{"id":"W2020166315","doi":"10.1145/1137983.1138020","title":"Using evolutionary annotations from change logs to enhance program comprehension","year":2006,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Source code; Computer science; Program comprehension; Workbench; Software evolution; Eclipse; Programming language; Software maintenance; Code (set theory); Software; Filter (signal processing); Evolutionary algorithm; Software engineering; Artificial intelligence; Software development; Software system; Set (abstract data type); Software construction; Visualization","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":[0.007976924,0.001881802,0.001141607,0.005721917,0.0008734543,0.003348321,0.002084737,0.002010997,0.004024949],"category_scores_gemma":[0.0777107,0.001047364,0.0006503239,0.002802171,0.0007392981,0.007963019,0.002274191,0.002345202,0.00175294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007245102,"about_ca_system_score_gemma":0.001553559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002779142,"about_ca_topic_score_gemma":0.004157132,"domain_scores_codex":[0.9952632,0.001859262,0.0003978942,0.000891829,0.001421052,0.000166769],"domain_scores_gemma":[0.8755213,0.09018888,0.008444573,0.0127231,0.01197209,0.001149931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001162243,0.001166128,0.02600373,0.00134093,0.0001136604,0.001623267,0.01471706,0.01278608,0.04550614,0.005883144,0.01503567,0.874662],"study_design_scores_gemma":[0.0004786131,0.001093089,0.03716521,0.0007370195,0.0003612775,0.002164958,0.00371116,0.6595663,0.1645195,0.03412965,0.09545571,0.0006175719],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1184096,0.0002566871,0.7927219,0.001007729,0.0001241786,0.0007141636,0.002405008,0.07979839,0.004562288],"genre_scores_gemma":[0.2716244,0.0002423719,0.7128579,0.0001911498,0.0001067073,0.0004858943,0.006310212,0.004585193,0.003596099],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007976924,"threshold_uncertainty_score":0.0421865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06159014107737977,"score_gpt":0.351656608422654,"score_spread":0.2900664673452742,"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."}}