{"id":"W4286331428","doi":"10.1109/saner53432.2022.00017","title":"On the Benefits of the Accelerate Metrics: An Industrial Survey at Vendasta","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; University of Saskatchewan","keywords":"Microservices; Computer science; Software deployment; Popularity; Process (computing); Context (archaeology); Productivity; Metric (unit); Process management; Software metric; Software development; Software; Software engineering; Software quality; Operations management; Engineering; Cloud computing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0166538,0.0002652768,0.0003329736,0.002299495,0.001096535,0.001695807,0.0005100965,0.0007256707,0.0007179018],"category_scores_gemma":[0.03217423,0.0002768121,0.0002199718,0.002299781,0.0006708938,0.001426488,0.001228857,0.0007708784,0.0002976243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00237062,"about_ca_system_score_gemma":0.002315691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01283318,"about_ca_topic_score_gemma":0.01872364,"domain_scores_codex":[0.9891877,0.005116278,0.000730123,0.0007405016,0.003184492,0.001041029],"domain_scores_gemma":[0.9468902,0.03213228,0.004952714,0.001063613,0.01236206,0.00259912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002418822,0.0004450217,0.5694513,0.0008646973,0.0000480271,0.0006282111,0.08796641,0.0005628998,0.01093143,0.001281101,0.006208934,0.3213702],"study_design_scores_gemma":[0.00002049753,0.001263062,0.7882651,0.0007923262,0.00006453861,0.0007989282,0.1359656,0.003167401,0.004556334,0.0003649798,0.0645997,0.0001414752],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910959,0.001240895,0.002365162,0.001299766,0.0000173051,0.00006393084,0.0001110538,0.00005801489,0.003748062],"genre_scores_gemma":[0.9932243,0.001582065,0.003077825,0.0004688776,0.00002179894,0.00007219694,0.000183199,0.00004703584,0.001322692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0166538,"threshold_uncertainty_score":0.08807474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07363896469653895,"score_gpt":0.2724287147298294,"score_spread":0.1987897500332904,"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."}}