{"id":"W2271119194","doi":"10.11575/prism/31125","title":"Using Structural Generalization to Discover Replacement Functionality for API Evolution","year":2014,"lang":"en","type":"article","venue":"Open MIND","topic":"Software Engineering Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Application programming interface; Software; Generalization; Java; Matching (statistics); Software engineering; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004796372,0.00006226086,0.00006764046,0.000050255,0.0001078539,0.0002849006,0.000445511,0.00002333109,0.00007329504],"category_scores_gemma":[0.0003046755,0.00005860036,0.00001948983,0.0001867823,0.000007331865,0.0004450309,0.0003055025,0.00003017603,0.00002561707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000182876,"about_ca_system_score_gemma":0.00006375762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007364867,"about_ca_topic_score_gemma":0.00001406069,"domain_scores_codex":[0.9991933,0.00003387579,0.0001118006,0.0002935714,0.0002059832,0.0001615145],"domain_scores_gemma":[0.9993992,0.00009563153,0.00002765898,0.000329044,0.00008693704,0.00006156437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002376151,0.00006811972,0.08250859,0.0000594562,0.00008590943,0.000001227573,0.001617833,0.6633022,0.02458297,0.05007811,0.004993422,0.1724646],"study_design_scores_gemma":[0.0004528055,0.0001210338,0.03998242,0.0000162665,0.000004641445,0.000004104954,0.000007270479,0.934662,0.00423837,0.0012366,0.01909419,0.0001803389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3020688,0.000003461151,0.6972,0.0001094053,0.0002605892,0.0002957015,0.000003473958,0.000003835904,0.00005474302],"genre_scores_gemma":[0.709883,4.258449e-8,0.2895463,0.0000270839,0.00009236844,0.00001958072,0.00001034414,0.000004778355,0.0004165169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4078142,"threshold_uncertainty_score":0.2747303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07569428380508372,"score_gpt":0.361097115499958,"score_spread":0.2854028316948742,"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."}}