{"id":"W2093607505","doi":"10.1145/2188286.2188304","title":"User-friendly approach for handling performance parameters during predictive software performance engineering","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reusability; Software engineering; Unified Modeling Language; Process (computing); Product (mathematics); Software; Set (abstract data type); Activity diagram; Middleware (distributed applications); Programming language; Database; Data mining","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006595381,0.0003285148,0.0003156277,0.0001866487,0.0002373004,0.00008087122,0.000823201,0.0001160536,0.000001399787],"category_scores_gemma":[0.0006872092,0.0003067253,0.0001004006,0.0003810848,0.00003868101,0.002549709,0.0003198185,0.0002674161,0.000005875147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001410796,"about_ca_system_score_gemma":0.00002702849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001181034,"about_ca_topic_score_gemma":3.754172e-8,"domain_scores_codex":[0.9979498,0.00002893517,0.0003095404,0.0004712693,0.0002669771,0.0009735097],"domain_scores_gemma":[0.9984004,0.0006163866,0.00009347075,0.0006187944,0.00008916736,0.000181762],"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":[0.00002534224,0.00003725797,0.008873533,0.0004022574,0.00004018645,4.074829e-7,0.0005467937,0.9823361,0.0006055479,0.0004600048,0.00002484219,0.006647704],"study_design_scores_gemma":[0.0007808075,0.0002841778,0.03257591,0.0001027269,0.00002333454,0.00006291333,0.00008409342,0.8855506,0.07913538,0.00002910527,0.0004628776,0.0009080308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2560586,0.0001600694,0.7414656,0.000006005997,0.0005660907,0.000331815,0.000002501447,0.001369444,0.00003990694],"genre_scores_gemma":[0.3905754,0.00002220508,0.6089787,0.00001222321,0.00009206826,0.0001811426,0.000003159619,0.00002813483,0.0001069574],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1345168,"threshold_uncertainty_score":0.9999385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0248158634041284,"score_gpt":0.2399792138350512,"score_spread":0.2151633504309228,"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."}}