{"id":"W2994143909","doi":"10.1109/icsme.2019.00099","title":"Improving the Robustness and Efficiency of Continuous Integration and Deployment","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Mitacs; McGill University","keywords":"Software deployment; Codebase; Pace; Computer science; Robustness (evolution); Software engineering; Deliverable; Software; DevOps; Process management; Software development; Risk analysis (engineering); Systems engineering; Engineering; Operating system; Business","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003208694,0.00005100335,0.00008141938,0.00002402589,0.000022188,0.00002744061,0.0001605977,0.00001722172,7.829242e-7],"category_scores_gemma":[0.000181636,0.00002997172,0.000007986855,0.00007600906,0.00003305138,0.000150119,0.0001502777,0.00004659767,3.178118e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000588998,"about_ca_system_score_gemma":0.000006993479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001283175,"about_ca_topic_score_gemma":0.000001457153,"domain_scores_codex":[0.9996098,0.0000306857,0.00008449895,0.0001332115,0.00006594724,0.00007581859],"domain_scores_gemma":[0.9993181,0.0004160333,0.00003776967,0.0001877983,0.00002779457,0.00001249779],"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":[0.000007669256,0.00002653111,0.003176766,0.00008556545,0.00001193091,0.000001495268,0.001984779,0.0698769,0.07993909,0.07297416,0.00001628721,0.7718988],"study_design_scores_gemma":[0.0004657541,0.0003003254,0.01137752,0.00004600036,0.000007637091,0.00003121258,0.0006021637,0.8836533,0.1004473,0.002764555,0.00004807328,0.0002561098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2860158,0.0001394266,0.7135198,0.00005547053,0.00008908549,0.00008324511,6.82735e-8,0.00006562711,0.00003138973],"genre_scores_gemma":[0.6305707,0.000007591961,0.369339,0.00001430429,0.000003002102,0.000002354865,4.035144e-8,0.000001983539,0.00006101596],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8137764,"threshold_uncertainty_score":0.1222211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01529404263464001,"score_gpt":0.2422527894987365,"score_spread":0.2269587468640965,"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."}}