{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004955681,0.0001191833,0.0001694252,0.00005058472,0.0001405616,0.0002090445,0.0003707255,0.00003743763,0.0000492004],"category_scores_gemma":[0.00008355338,0.00007432076,0.00003241037,0.0004245878,0.00002413307,0.000745283,0.0001231747,0.0001438292,0.00007976167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003806865,"about_ca_system_score_gemma":0.0001833895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005585142,"about_ca_topic_score_gemma":0.0000941847,"domain_scores_codex":[0.9985258,0.0001742116,0.0001883776,0.0005387948,0.0003717255,0.0002010778],"domain_scores_gemma":[0.9983937,0.0002069396,0.0001273213,0.001018561,0.000144803,0.0001086358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002334362,0.001978319,0.9877059,0.00002637212,0.00004572477,0.0002534953,0.002740117,0.001639133,0.00002032983,0.00006096956,0.0004632667,0.005042983],"study_design_scores_gemma":[0.002742992,0.01044767,0.7288809,0.0001089877,0.0000935334,0.0002727542,0.0198698,0.2224063,0.004158828,0.0001897034,0.009702387,0.001126171],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9658509,0.00002193349,0.03146324,0.0004371369,0.0001389751,0.0002396073,3.858293e-7,0.0002458578,0.00160192],"genre_scores_gemma":[0.9915256,0.000001985908,0.00753767,0.0005056072,0.00006014427,0.0000307107,0.000001316393,0.00000572167,0.0003312452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.258825,"threshold_uncertainty_score":0.3030713,"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."}}