{"id":"W11840263","doi":"10.1038/sj.leu.2402329","title":"From Products to Product Lines Using Model Matching and Refactoring.","year":2010,"lang":"en","type":"article","venue":"Leukemia","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Code refactoring; Computer science; Software product line; Unified Modeling Language; Product (mathematics); Set (abstract data type); Matching (statistics); Quality (philosophy); Variable (mathematics); Programming language; Data mining; Software; Mathematics; Software development","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":[0.004079862,0.001683348,0.00120048,0.002511504,0.0004712071,0.00211377,0.001991933,0.001036335,0.0131902],"category_scores_gemma":[0.006173879,0.001326556,0.002375264,0.002468617,0.0006317716,0.001904296,0.002203536,0.002034386,0.01952391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008009864,"about_ca_system_score_gemma":0.001200518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001688459,"about_ca_topic_score_gemma":0.001343108,"domain_scores_codex":[0.9976096,0.0003227363,0.0003183488,0.000779313,0.0008109672,0.0001589707],"domain_scores_gemma":[0.997407,0.0004835522,0.0003176422,0.001241331,0.0004531178,0.00009726243],"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":[0.0008016846,0.0004188604,0.003817539,0.001817638,0.0002907318,0.001351238,0.0004363806,0.01732371,0.38907,0.012674,0.01758519,0.5544131],"study_design_scores_gemma":[0.0001095435,0.0007274867,0.001931518,0.0002917759,0.000307844,0.001630744,0.0001241596,0.02930033,0.4961796,0.01185578,0.457381,0.0001602368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02844366,0.002706829,0.9186722,0.0003840617,0.0004905924,0.001330938,0.007371578,0.02816166,0.01243844],"genre_scores_gemma":[0.09641709,0.00411744,0.8357269,0.0003350433,0.00004601307,0.001151766,0.03143112,0.005985579,0.02478903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0131902,"threshold_uncertainty_score":0.04412568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05397786699320867,"score_gpt":0.3085524249807872,"score_spread":0.2545745579875786,"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."}}