{"id":"W2898497187","doi":"10.1007/978-3-030-03418-4_5","title":"Using Umple to Synergistically Process Features, Variants, UML Models and Classic Code","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Programming language; Class diagram; Unified Modeling Language; Dependency (UML); Code generation; Syntax; Construct (python library); Abstract syntax tree; Sequence diagram; Activity diagram; Abstract syntax; Separation of concerns; Code (set theory); Source code; Process (computing); Documentation; Software engineering; Semantics (computer science); Artificial intelligence; Software; Set (abstract data type); Parsing","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":[0.002088758,0.001130205,0.0005512179,0.001817594,0.0008956788,0.004381396,0.001617191,0.001094977,0.009889324],"category_scores_gemma":[0.0115523,0.001008553,0.001728562,0.001502738,0.00118024,0.007358445,0.006427987,0.002362145,0.004885334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006794854,"about_ca_system_score_gemma":0.0009142299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009601246,"about_ca_topic_score_gemma":0.002426962,"domain_scores_codex":[0.9982775,0.000436023,0.0001043577,0.0004063313,0.0006641959,0.0001115944],"domain_scores_gemma":[0.9965616,0.001389605,0.000275476,0.001311861,0.0003255558,0.0001359397],"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":[0.0003030971,0.0001804536,0.00215092,0.0004870176,0.0000942902,0.0006170874,0.002256778,0.01512549,0.0250293,0.1572114,0.0115099,0.7850342],"study_design_scores_gemma":[0.00006098789,0.0002357523,0.001101579,0.0004054725,0.000176033,0.00135238,0.0007527538,0.352385,0.08309899,0.3217697,0.2385403,0.0001211055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005433602,0.0001276724,0.9792128,0.0001220923,0.00005336864,0.0000636096,0.00009180727,0.007437368,0.007457653],"genre_scores_gemma":[0.0606358,0.0002165947,0.9221056,0.0001301104,0.00003039496,0.0001010377,0.0007103651,0.003692129,0.01237815],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009889324,"threshold_uncertainty_score":0.03308314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07258018217045781,"score_gpt":0.3255720829345026,"score_spread":0.2529919007640448,"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."}}