{"id":"W1971926166","doi":"10.1145/2752489.2752490","title":"Reliability Analysis and Quality Impact Prediction in Application Architecture evolution","year":2015,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Computer science; Reliability (semiconductor); Hidden Markov model; Markov chain; Architecture; Quality (philosophy); Scale (ratio); Markov model; Markov process; Machine learning; Reliability engineering; Data mining; Artificial intelligence; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019693,0.0006069289,0.0003931101,0.001972513,0.000208612,0.0005064331,0.0005742303,0.0005461081,0.0005028016],"category_scores_gemma":[0.01202739,0.0003610735,0.0004339207,0.001027354,0.0003453414,0.000782828,0.0003653919,0.0006264186,0.0001385789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006843595,"about_ca_system_score_gemma":0.0004769182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006496134,"about_ca_topic_score_gemma":0.005311538,"domain_scores_codex":[0.9989042,0.0004500659,0.00004888433,0.0001724186,0.0003602705,0.00006408221],"domain_scores_gemma":[0.9923124,0.005293311,0.000960757,0.0005040088,0.0008362312,0.00009333813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001013728,0.0001041296,0.04363705,0.0001116146,0.00006995002,0.000112201,0.0002330983,0.8098784,0.0078847,0.002380322,0.0004150919,0.135072],"study_design_scores_gemma":[0.000002377702,0.00002962535,0.006074512,0.000005780144,0.00000907809,0.0000252078,0.00001078038,0.9908978,0.001284622,0.00153773,0.0001165168,0.000005878794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4157176,0.0006096618,0.5803722,0.0002631921,0.00001539042,0.00006555513,0.0002118641,0.001046895,0.0016977],"genre_scores_gemma":[0.9541033,0.000135372,0.04519275,0.00001626659,0.000009149877,0.00003056706,0.0001497316,0.00004592923,0.0003169471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006496134,"threshold_uncertainty_score":0.01291662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01442270682971548,"score_gpt":0.2891055729990214,"score_spread":0.2746828661693059,"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."}}