{"id":"W2019628947","doi":"10.1109/icsm.2012.6405285","title":"Models are code too: Near-miss clone detection for Simulink models","year":2012,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; General Motors of Canada; Technische Universität München","keywords":"Computer science; Source code; clone (Java method); Code (set theory); Matching (statistics); Graph; Detector; Programming language; Graphical model; Identification (biology); Theoretical computer science; Artificial intelligence; Mathematics","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":[0.0005097677,0.000139576,0.0001495054,0.0000824693,0.0001446404,0.0001741437,0.0006052943,0.0001011097,0.000007416031],"category_scores_gemma":[0.000224479,0.0001306794,0.00007451662,0.0002986391,0.00002244131,0.001490234,0.0002084137,0.0001545532,0.00004216077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009641238,"about_ca_system_score_gemma":0.00004251036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003889959,"about_ca_topic_score_gemma":0.000007366923,"domain_scores_codex":[0.9985039,0.00002373592,0.0001677026,0.0003037053,0.000342158,0.0006587814],"domain_scores_gemma":[0.998409,0.0005706696,0.000035007,0.0005728789,0.0001635831,0.0002488216],"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":[0.00001879535,0.0001077394,0.0004321765,0.00006124615,0.00002773281,0.000001820415,0.0005890806,0.9513359,0.0009199907,0.01218236,0.002650253,0.03167288],"study_design_scores_gemma":[0.0002513471,0.00004445181,0.0002602748,0.000007263447,0.000002749662,0.000005750333,0.00000892704,0.9867984,0.005158857,0.006179743,0.001110618,0.000171598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02409477,0.0001590126,0.9736807,0.0003550008,0.0004216135,0.0003352637,0.000004451764,0.0007044626,0.0002446842],"genre_scores_gemma":[0.8461357,0.000007319708,0.1528192,0.0001188087,0.0001367333,0.00007831406,0.00000131974,0.00002147198,0.0006811082],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8220409,"threshold_uncertainty_score":0.5328951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06396359254443656,"score_gpt":0.2926016956512792,"score_spread":0.2286381031068427,"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."}}