{"id":"W2155915714","doi":"10.1109/splc.2011.27","title":"Automatic Derivation of a Product Performance Model from a Software Product Line Model","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Unified Modeling Language; Model transformation; Programming language; Software product line; Software engineering; Software; Software development; 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":[0.0003470979,0.0001891136,0.0002635787,0.0001137627,0.00004647446,0.00001492767,0.0007936776,0.00004071969,0.000007544887],"category_scores_gemma":[0.001023909,0.0001623043,0.00004510543,0.0003246301,0.00004866471,0.0009531074,0.0002687966,0.0001286954,0.000007261322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003861688,"about_ca_system_score_gemma":0.0001182639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001414731,"about_ca_topic_score_gemma":0.000001223551,"domain_scores_codex":[0.9986298,0.00003611353,0.0003530766,0.0004867261,0.0002438765,0.0002504253],"domain_scores_gemma":[0.998473,0.0001359805,0.0001440987,0.001028721,0.0001708564,0.00004733303],"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":[0.000006370899,0.00005920912,0.0002567895,0.000087222,0.00001456542,6.157082e-7,0.001873212,0.9037831,0.002517513,0.001462254,0.00004019993,0.08989891],"study_design_scores_gemma":[0.0001031185,0.00004069876,0.001306863,0.00003422343,0.0000060736,0.0000020146,0.000004267967,0.8784389,0.09363871,0.02625123,0.000001071761,0.000172754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1725659,0.0001078572,0.8258587,0.00004062276,0.0001101209,0.0002119485,0.000002696143,0.001049616,0.00005249771],"genre_scores_gemma":[0.4014145,0.000009521109,0.5983966,0.0000249876,0.00001578895,0.00002519528,0.000001950331,0.00001155234,0.00009988408],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2288486,"threshold_uncertainty_score":0.6618577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1048617323998261,"score_gpt":0.270819739072591,"score_spread":0.1659580066727649,"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."}}