{"id":"W1676236699","doi":"10.1007/978-3-642-31491-9_9","title":"Model Transformations for Migrating Legacy Models: An Industrial Case Study","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Model-Driven Software Engineering Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"AUTOSAR; Metamodeling; Automotive industry; Computer science; Software engineering; Model transformation; Modeling language; Legacy system; Software development; Software; Systems engineering; Unified Modeling Language; Software architecture; Manufacturing engineering; Programming language; Engineering; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002900363,0.0005676862,0.0003048347,0.0007457255,0.001027014,0.001721625,0.001758411,0.001406376,0.002774871],"category_scores_gemma":[0.008256217,0.0003724189,0.0005954221,0.00140805,0.0007983144,0.001895242,0.001088711,0.001242071,0.0004924754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008993355,"about_ca_system_score_gemma":0.001315952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003864228,"about_ca_topic_score_gemma":0.006170629,"domain_scores_codex":[0.9982063,0.0007678218,0.00009203617,0.000154469,0.0006214622,0.0001579287],"domain_scores_gemma":[0.9941684,0.003413218,0.0002840041,0.001426986,0.000578097,0.0001293537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001727647,0.004782639,0.0211164,0.001190713,0.0001486551,0.009353257,0.01010653,0.1895783,0.0515155,0.06839699,0.01264635,0.629437],"study_design_scores_gemma":[0.0008029726,0.002010833,0.01240884,0.0003302803,0.0004555551,0.005606832,0.006629351,0.7147757,0.1253016,0.0414277,0.09008745,0.0001629529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7916633,0.0004398981,0.1767339,0.0009968844,0.00006371988,0.0005289987,0.000443895,0.002653739,0.02647564],"genre_scores_gemma":[0.8731699,0.0003748307,0.1183777,0.00008436797,0.00001284606,0.000133046,0.0005648158,0.0004451877,0.006837306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003864228,"threshold_uncertainty_score":0.01533878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09122904825446307,"score_gpt":0.2931293093861676,"score_spread":0.2019002611317045,"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."}}