{"id":"W2122185633","doi":"10.1145/2568225.2568267","title":"Lifting model transformations to product lines","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Code refactoring; Software product line; Computer science; Model transformation; Software engineering; Software; Product (mathematics); Source lines of code; Set (abstract data type); Software development; Model-driven architecture; Feature model; Programming language; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003671992,0.00007083429,0.00008141856,0.0000680126,0.00005762609,0.00003949111,0.000409307,0.00001483444,9.030492e-7],"category_scores_gemma":[0.0009164877,0.00005979949,0.00002104665,0.0002363208,0.000006266845,0.0004029408,0.00007106388,0.00005626113,0.0000297704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001046002,"about_ca_system_score_gemma":0.00001220578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001230486,"about_ca_topic_score_gemma":0.000001321457,"domain_scores_codex":[0.9994298,0.00002472333,0.0001191635,0.0001762734,0.00009114938,0.0001588882],"domain_scores_gemma":[0.9993424,0.0002003506,0.00001306503,0.0003423645,0.00005036939,0.00005144741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[2.468235e-7,0.000003163543,0.000004101957,0.000005279566,0.000001014812,5.445811e-8,0.0004504049,0.8912212,0.001121552,0.04420574,0.0001653924,0.06282184],"study_design_scores_gemma":[0.00004035371,0.00001768812,0.00005174622,0.000006068969,8.645335e-7,0.00000212726,0.000005474889,0.9561979,0.01269434,0.02873352,0.002126754,0.0001231896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001173296,0.00000978379,0.9942361,0.001882002,0.0001700775,0.00008608212,2.489892e-7,0.001015175,0.001427208],"genre_scores_gemma":[0.1103098,0.000001133047,0.8889785,0.0003003083,0.00004337302,0.00001408132,2.491394e-7,0.000004885778,0.0003476357],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1091365,"threshold_uncertainty_score":0.2438552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04555032720366534,"score_gpt":0.2974835235763194,"score_spread":0.251933196372654,"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."}}