{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001979259,0.00009702081,0.0001982967,0.0002202513,0.00004112447,0.00004197553,0.0002006388,0.00009230752,0.000002857491],"category_scores_gemma":[0.0001514261,0.00006800434,0.00007413475,0.001447811,0.00004211789,0.0004210937,0.00008603256,0.0001141333,0.00001023223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003013742,"about_ca_system_score_gemma":0.0001097895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005699399,"about_ca_topic_score_gemma":0.0006262295,"domain_scores_codex":[0.9986541,0.000185851,0.0003284525,0.0004168732,0.0002587381,0.0001559592],"domain_scores_gemma":[0.9989425,0.00006801249,0.00008053497,0.0006619958,0.0001294326,0.000117541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001311459,0.00004300522,0.9825796,0.00001468714,0.00001616252,7.567715e-8,0.0003390946,0.008944945,0.00006671855,0.0006200887,0.00002723993,0.007335241],"study_design_scores_gemma":[0.0002005179,0.00004427665,0.8720559,0.000002379943,0.00001152709,0.000001568904,0.00002604647,0.1232139,0.00004916878,0.004272731,0.00005273146,0.00006925439],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.472839,0.00002697551,0.5264936,0.00008981802,0.00003827079,0.0001420039,0.000002175204,0.0001027015,0.0002654859],"genre_scores_gemma":[0.9936691,0.000001893954,0.006216566,0.00001812329,0.00002615633,0.00002936134,0.000008817603,0.000001913647,0.00002806822],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5208302,"threshold_uncertainty_score":0.8615825,"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."}}