{"id":"W3177473871","doi":"10.1109/mobilesoft52590.2021.00013","title":"Logging Practices with Mobile Analytics: An Empirical Study on Firebase","year":2021,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Analytics; Logging; Computer science; Android (operating system); Debugging; Mobile device; Software analytics; Database; World Wide Web; Software; Operating system; Software development; Component-based software engineering","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":[0.01004661,0.000336009,0.0004222569,0.002425353,0.001425289,0.003047571,0.001446455,0.001114534,0.001547569],"category_scores_gemma":[0.06892379,0.0005193482,0.0002761958,0.002412582,0.00198731,0.005133018,0.001956815,0.001990072,0.0006293782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00126035,"about_ca_system_score_gemma":0.001470439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005381415,"about_ca_topic_score_gemma":0.008598007,"domain_scores_codex":[0.992679,0.002947144,0.0005721449,0.0008447053,0.002405678,0.000551291],"domain_scores_gemma":[0.8738627,0.08109833,0.02090276,0.006742599,0.01434853,0.003045083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00033418,0.003156689,0.711782,0.0004839101,0.00008028004,0.001357506,0.1905709,0.000640105,0.00314577,0.001112867,0.002014437,0.0853214],"study_design_scores_gemma":[0.00004434446,0.001809112,0.7556258,0.0005276358,0.00007164095,0.001722173,0.2141923,0.006999522,0.003316958,0.0006657584,0.01489724,0.0001274568],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984398,0.00006323969,0.0005048659,0.0001037809,0.000002583965,0.00006809131,0.00006690778,0.00002249048,0.0007281774],"genre_scores_gemma":[0.9971768,0.0001582961,0.001488499,0.00008902137,0.000006243609,0.0001082993,0.0001480293,0.00003971445,0.0007850518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01004661,"threshold_uncertainty_score":0.05313224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04789393331643494,"score_gpt":0.3632978426544203,"score_spread":0.3154039093379853,"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."}}