{"id":"W4320009943","doi":"10.2139/ssrn.4341190","title":"Test Plan Generation for Live Testing of Cloud Services","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Test plan; Test (biology); Plan (archaeology); Software deployment; Production (economics); Schedule; Computer science; Test Management Approach; Task (project management); Cloud computing; Test case; Reliability engineering; Engineering; Systems engineering; Software engineering; Operating system; Software; Machine learning","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":[0.00195444,0.00008586379,0.0001334087,0.00009181755,0.0002180223,0.00005720645,0.0004653332,0.00005151471,9.610374e-7],"category_scores_gemma":[0.0001470503,0.0000680474,0.00006483571,0.0004024587,0.00001415931,0.0003022739,0.00005637534,0.0003183637,0.0000262299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001616644,"about_ca_system_score_gemma":0.000837013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004079712,"about_ca_topic_score_gemma":0.0001371115,"domain_scores_codex":[0.9984031,0.00003153886,0.0002866235,0.0001727926,0.000190925,0.0009150725],"domain_scores_gemma":[0.9990957,0.0002725049,0.0001951588,0.0001987297,0.0002000207,0.00003785251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005821346,0.000329423,0.4957614,0.0006467958,0.000344033,0.000008609989,0.007555529,0.008633904,0.0338058,0.1029228,0.0016896,0.3482439],"study_design_scores_gemma":[0.001888779,0.003734199,0.01573331,0.0002703879,0.00004893109,0.0006613758,0.002648613,0.7925844,0.006168282,0.174338,0.001303451,0.0006202756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9001539,0.0003298288,0.09812289,0.0003116358,0.0007003036,0.000187734,0.000003281725,0.0001396585,0.00005073205],"genre_scores_gemma":[0.9970065,0.0001845759,0.001936027,0.00003085175,0.0006482179,0.00001057539,0.000004646616,0.000007740667,0.0001708118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7839506,"threshold_uncertainty_score":0.2774893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02232859703515864,"score_gpt":0.2508414005541387,"score_spread":0.2285128035189801,"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."}}