{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00195714,0.001896075,0.0009990409,0.005522381,0.0009845791,0.002496715,0.002337245,0.001628184,0.005140879],"category_scores_gemma":[0.01422231,0.0009919797,0.001025489,0.004915163,0.00065388,0.00488363,0.002330429,0.001941521,0.00609033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005337101,"about_ca_system_score_gemma":0.00236815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00256188,"about_ca_topic_score_gemma":0.003339358,"domain_scores_codex":[0.9964646,0.0006157776,0.0005295911,0.0007841138,0.001428212,0.000177742],"domain_scores_gemma":[0.992218,0.003295221,0.0006781464,0.002038592,0.001619464,0.000150543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002665176,0.0002677133,0.006875818,0.0005156696,0.00009399558,0.0004641241,0.0004970455,0.008366453,0.0228581,0.004890662,0.05866709,0.8962368],"study_design_scores_gemma":[0.0001610836,0.0002925982,0.01321601,0.000237527,0.0001815452,0.002336652,0.0009277625,0.6923079,0.1047828,0.03963618,0.1456504,0.0002694233],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01041362,0.0003377835,0.8628927,0.0003626558,0.0001114087,0.0003930583,0.004012337,0.1196748,0.001801717],"genre_scores_gemma":[0.09462366,0.0003108536,0.8865202,0.0003225773,0.00009458527,0.0006206296,0.01070957,0.00402482,0.002773106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005522381,"threshold_uncertainty_score":0.01719791,"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."}}