{"id":"W10897080","doi":"10.1002/1098-108x(200009)28:2<181::aid-eat7>3.0.co;2-k","title":"Detecting model refactoring opportunities using heuristic search","year":2011,"lang":"en","type":"article","venue":"Conference of the Centre for Advanced Studies on Collaborative Research","topic":"Software Engineering Research","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; École de Technologie Supérieure","funders":"","keywords":"Code refactoring; Computer science; Model-driven architecture; Heuristic; Process (computing); Software engineering; Artificial intelligence; Software development; Software; 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":[0.001859452,0.0002377525,0.0003880572,0.0003349681,0.0007804816,0.00009255253,0.001732402,0.00006603445,0.000004276985],"category_scores_gemma":[0.008327751,0.0001788485,0.00008745629,0.001444509,0.0005435604,0.0004119014,0.001241647,0.0005398457,0.000002698107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004376287,"about_ca_system_score_gemma":0.0009842132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001179886,"about_ca_topic_score_gemma":0.00002328402,"domain_scores_codex":[0.9966614,0.0003985881,0.0003803071,0.0005673802,0.001113509,0.0008788074],"domain_scores_gemma":[0.9895102,0.003073931,0.0001329952,0.0009563243,0.006171932,0.0001546646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00152181,0.0008495405,0.005023168,0.0025318,0.001339394,0.0000737937,0.166497,0.1759338,0.08669983,0.487957,0.0009513325,0.07062151],"study_design_scores_gemma":[0.001402211,0.0008736521,0.0004537659,0.001460041,0.00001987932,0.000003315244,0.03974927,0.4040863,0.5318913,0.01898981,0.0003968561,0.0006735579],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.75647,0.00148405,0.2340753,0.001301144,0.001305023,0.003459893,0.0001364692,0.0002750641,0.001493077],"genre_scores_gemma":[0.9630572,0.0002283194,0.03576746,0.00000785568,0.00003107283,0.0000846188,4.937947e-7,0.00002731765,0.0007956109],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4689672,"threshold_uncertainty_score":0.9969698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5083945867567765,"score_gpt":0.4311035929333741,"score_spread":0.07729099382340243,"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."}}