{"id":"W4301606717","doi":"10.1109/icsme55016.2022.00009","title":"An Effective Approach for Parsing Large Log Files","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Parsing; Computer science; Transaction log; Software; Data mining; String (physics); Benchmark (surveying); Matching (statistics); Pattern matching; Web log analysis software; Database; Artificial intelligence; Programming language; Database transaction; Web server; Operating system; The Internet; Statistics","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":[0.001447325,0.0002578074,0.0003948892,0.0001077542,0.0003570201,0.0002074666,0.001551107,0.0002236789,0.00005333237],"category_scores_gemma":[0.00004747594,0.0002078034,0.0002293653,0.0001384473,0.00002958075,0.0002767721,0.00155631,0.0004538636,0.000006120531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001733276,"about_ca_system_score_gemma":0.0001309961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006909127,"about_ca_topic_score_gemma":0.000002678445,"domain_scores_codex":[0.9977546,0.0002216843,0.0002917952,0.00104151,0.0002929814,0.0003974553],"domain_scores_gemma":[0.9979317,0.0002155617,0.0001496069,0.001508092,0.0001049217,0.00009009158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003191441,0.007365768,0.09642495,0.01773905,0.001062146,0.0000300758,0.03382172,0.1763901,0.0002253625,0.1174756,0.03758438,0.5115617],"study_design_scores_gemma":[0.0006123841,0.0003784912,0.009963888,0.00005617216,0.00002684663,0.000009712159,0.0003002396,0.9708502,0.000395795,0.009384011,0.007277473,0.0007447999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01963868,0.0001520442,0.9734697,0.00005242197,0.001468023,0.002047858,0.00006538344,0.0005972334,0.002508623],"genre_scores_gemma":[0.7572638,0.000007506679,0.2397348,0.0001821254,0.0002512907,0.002092112,0.0002302115,0.000021612,0.0002165896],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7944601,"threshold_uncertainty_score":0.8473975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01762067912088179,"score_gpt":0.2887153565170313,"score_spread":0.2710946773961495,"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."}}