{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008084266,0.0007981578,0.0005621216,0.001191446,0.0004279424,0.0007576286,0.001052777,0.0009646156,0.001325299],"category_scores_gemma":[0.01330792,0.0004874097,0.0006977151,0.000773298,0.0007403797,0.002030887,0.0011968,0.0009117498,0.000418565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006310341,"about_ca_system_score_gemma":0.0008244208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00333869,"about_ca_topic_score_gemma":0.005526478,"domain_scores_codex":[0.9985145,0.0002876287,0.00007702554,0.0003253972,0.000728836,0.0000666392],"domain_scores_gemma":[0.9937009,0.002660769,0.00117003,0.001597063,0.0007259999,0.0001453039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001034795,0.0002457796,0.03411351,0.0005345857,0.000209461,0.002473526,0.001780902,0.4935073,0.09227293,0.04812837,0.004158685,0.3215401],"study_design_scores_gemma":[0.00001891254,0.00009653443,0.001425845,0.00002084791,0.00003504491,0.0003512031,0.0001031177,0.9425472,0.03472834,0.01726609,0.003383737,0.00002305992],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1214678,0.00009186671,0.8683595,0.0001246563,0.00001851589,0.00007012004,0.0002656878,0.008441383,0.001160459],"genre_scores_gemma":[0.663051,0.0000970809,0.3330683,0.00008155308,0.00000733774,0.00008087383,0.0007928471,0.0008989067,0.001922027],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00333869,"threshold_uncertainty_score":0.006638527,"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."}}