{"id":"W3168361240","doi":"10.1007/s10664-021-10111-4","title":"PRINS: scalable model inference for component-based system logs","year":2022,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Fonds National de la Recherche Luxembourg","keywords":"Computer science; Scalability; Component (thermodynamics); Inference; Data mining; Software; Component-based software engineering; Software system; Process (computing); Implementation; Software engineering; Machine learning; Artificial intelligence; Database; Programming language","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":[0.0005887353,0.0002384812,0.0003311647,0.0001413635,0.0004002447,0.00008738963,0.0009980495,0.00007800073,0.000008557698],"category_scores_gemma":[0.0002590124,0.0002275157,0.0001683981,0.000526773,0.00001962098,0.0002700476,0.0004632587,0.0003020112,0.00001722847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004882757,"about_ca_system_score_gemma":0.0002046562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001433722,"about_ca_topic_score_gemma":4.071857e-7,"domain_scores_codex":[0.9979761,0.00004373017,0.0004104954,0.0005530903,0.0004877399,0.000528829],"domain_scores_gemma":[0.9984079,0.0005441838,0.00008651539,0.0006963549,0.00009817499,0.0001668758],"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":[0.00001003604,0.00007322487,0.01673984,0.0003802555,0.00001312561,0.000004600875,0.0001913855,0.9803088,0.00005064162,0.0008864209,0.0006225886,0.0007190708],"study_design_scores_gemma":[0.0004734891,0.0001008635,0.001928288,0.00004836346,0.000006549776,0.00000976041,0.00001625715,0.9901599,0.0002175981,0.00006813185,0.006665257,0.0003054921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07818785,0.0001116014,0.9185867,0.0001826127,0.0007458301,0.0004821502,0.00002348867,0.001662671,0.00001713698],"genre_scores_gemma":[0.8571442,5.514617e-7,0.1418911,0.000219682,0.00005959272,0.0005773597,0.00001514937,0.00002649533,0.00006587579],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7789564,"threshold_uncertainty_score":0.9277822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02421827605858904,"score_gpt":0.26245946344478,"score_spread":0.238241187386191,"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."}}