{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002588847,0.0001474215,0.0001575649,0.00007067616,0.00007669698,0.00009522337,0.0004621269,0.00005094291,4.961728e-7],"category_scores_gemma":[0.001060921,0.0001364361,0.0000144019,0.0002130425,0.00002185276,0.000459293,0.000392957,0.0002972511,0.000004342225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008056892,"about_ca_system_score_gemma":0.0001081941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001147835,"about_ca_topic_score_gemma":0.000006515459,"domain_scores_codex":[0.9989272,0.00002190918,0.0001379796,0.0005328733,0.0001399731,0.0002400533],"domain_scores_gemma":[0.9989553,0.0001598821,0.00004212165,0.0006968156,0.00006379207,0.00008205636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002797432,0.00000328163,0.00009200539,0.00001619133,0.000004839414,0.000004668905,0.001561849,0.415976,0.5658338,0.0009786087,0.00003363285,0.01549233],"study_design_scores_gemma":[0.0003251425,0.00003868277,0.003527617,0.0001001942,0.00001479359,0.00004643503,0.00004092648,0.4006914,0.46259,0.1298548,0.001755966,0.00101408],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4711643,0.00003503735,0.5276464,0.0001579221,0.0006454116,0.00006622251,0.000001144014,0.0002760168,0.000007449051],"genre_scores_gemma":[0.2933534,0.000001901203,0.7061677,0.00005309196,0.000334938,0.000004664082,5.171182e-7,0.00001328646,0.0000704802],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1785213,"threshold_uncertainty_score":0.5563701,"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."}}