{"id":"W3147021188","doi":"10.1109/iwsc.2012.6227873","title":"Near-miss model clone detection for Simulink models","year":2012,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; General Motors of Canada","keywords":"Computer science; Leverage (statistics); clone (Java method); Granularity; Source code; Identification (biology); Detector; Artificial intelligence; Data mining; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001555815,0.000964982,0.0007930517,0.001705778,0.0005060982,0.001455141,0.001445463,0.001189828,0.001836858],"category_scores_gemma":[0.01900428,0.0005744119,0.001198252,0.0007372269,0.0008849393,0.002853059,0.001623514,0.001533456,0.000658942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001366257,"about_ca_system_score_gemma":0.001061891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002854787,"about_ca_topic_score_gemma":0.005327968,"domain_scores_codex":[0.9966714,0.000543643,0.0002004174,0.0005300401,0.001911632,0.0001428099],"domain_scores_gemma":[0.9848477,0.006574905,0.002539623,0.003326472,0.002514902,0.0001963207],"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":[0.001183196,0.0003092679,0.04779734,0.00116087,0.0002752315,0.002807349,0.003304529,0.2212868,0.2121302,0.04676464,0.004853178,0.4581274],"study_design_scores_gemma":[0.00002646888,0.0002273899,0.002440902,0.00008007587,0.00006969078,0.0007588575,0.0002309113,0.8093362,0.1632233,0.01310276,0.01044509,0.00005836482],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07625918,0.0001438815,0.908253,0.0001337685,0.00003125825,0.0001029082,0.0002098277,0.01393569,0.0009304473],"genre_scores_gemma":[0.4598381,0.0001377947,0.534581,0.0001328915,0.00001219717,0.0001053976,0.0006877981,0.00231154,0.002193204],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002854787,"threshold_uncertainty_score":0.009912968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05080366454702209,"score_gpt":0.2929140153861381,"score_spread":0.242110350839116,"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."}}