{"id":"W2154306155","doi":"10.1109/hase.2008.16","title":"Detection and Diagnosis of Recurrent Faults in Software Systems by Invariant Analysis","year":2008,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Discriminative model; Correlation; Software; Metric (unit); Invariant (physics); Fault detection and isolation; Machine learning; Data mining; Software system; Software metric; Artificial intelligence; Software bug; Software quality; Software development; Mathematics; Engineering","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":[0.0003343854,0.00009270272,0.0002728563,0.0002253122,0.00006215404,0.0000162243,0.0002043138,0.00007064004,0.000003650219],"category_scores_gemma":[0.00007260293,0.00007048022,0.0000590019,0.001116698,0.00003960862,0.0002844165,0.00008396213,0.00006862529,0.00000362686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004558662,"about_ca_system_score_gemma":0.00002392652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001762597,"about_ca_topic_score_gemma":0.0002396494,"domain_scores_codex":[0.9989102,0.00008230936,0.0003579908,0.0003010352,0.0002069261,0.0001415665],"domain_scores_gemma":[0.9992884,0.0001382652,0.0001032439,0.0003443966,0.00007191141,0.00005381704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005864965,0.0000918917,0.9858723,0.00008591767,0.00005420083,0.000003422534,0.0006337783,0.0003942622,0.00003949063,0.00003527404,0.0001766483,0.0126069],"study_design_scores_gemma":[0.0007728201,0.0003111245,0.8400586,0.0001125187,0.00006543739,0.00004186999,0.0001300915,0.1525237,0.004821749,0.00005790704,0.000721681,0.0003825384],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7974255,0.0006738082,0.2014238,0.00003121556,0.0001775099,0.0001642298,0.000003542958,0.00006752896,0.00003284818],"genre_scores_gemma":[0.9981448,0.0003238176,0.001425263,0.00001170079,0.000009301461,0.00005320049,0.00000169426,0.000002458288,0.00002776844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2007193,"threshold_uncertainty_score":0.28741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01235566452900682,"score_gpt":0.2227109798795166,"score_spread":0.2103553153505097,"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."}}