{"id":"W4240790600","doi":"10.1145/1932682.1869518","title":"Refactoring references for library migration","year":2010,"lang":"en","type":"article","venue":"ACM SIGPLAN Notices","topic":"Software Engineering Research","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Code refactoring; Computer science; Programming language; Declaration; Set (abstract data type); Transformation (genetics); Source code; Feature (linguistics); Software engineering; Code (set theory); Key (lock); Program transformation; Software; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007159416,0.001165464,0.0008023924,0.002293733,0.001493942,0.002319698,0.003800739,0.002740552,0.005759165],"category_scores_gemma":[0.04677531,0.001144098,0.001278156,0.002131279,0.001269764,0.005833359,0.004998804,0.002850133,0.003174688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008876819,"about_ca_system_score_gemma":0.003195351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002307379,"about_ca_topic_score_gemma":0.003898063,"domain_scores_codex":[0.9925374,0.002752176,0.0006373571,0.0008844085,0.00274938,0.0004391818],"domain_scores_gemma":[0.951265,0.01708663,0.004108234,0.02108046,0.005894777,0.0005648571],"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":[0.00042182,0.0004679915,0.01435135,0.001311374,0.0001254641,0.001366191,0.003501878,0.01840528,0.06596809,0.03035345,0.02532589,0.8384012],"study_design_scores_gemma":[0.0003707758,0.0007210265,0.01014374,0.001288085,0.0004798603,0.003448884,0.001177003,0.2244144,0.2560139,0.05623401,0.4451804,0.0005279322],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06538603,0.001571625,0.8656142,0.001055568,0.0002997353,0.0003729133,0.0003313188,0.05839985,0.00696877],"genre_scores_gemma":[0.2072413,0.0007905479,0.7782799,0.0004590044,0.0001089724,0.0002910387,0.0009727634,0.006427214,0.005429244],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007159416,"threshold_uncertainty_score":0.03786302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03467563794292089,"score_gpt":0.28359976719964,"score_spread":0.2489241292567191,"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."}}