{"id":"W1516438975","doi":"10.1002/stvr.1573","title":"Anomaly detection in performance regression testing by transaction profile estimation","year":2015,"lang":"en","type":"article","venue":"Software Testing Verification and Reliability","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Science Foundation Ireland","keywords":"Computer science; Regression testing; Workload; Anomaly detection; Data mining; Software performance testing; Software regression; Regression analysis; Regression; Non-regression testing; Software; Machine learning; Software quality; Statistics; Software system; Operating system; Software development","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003303206,0.0007919786,0.0007953395,0.003286486,0.0002945382,0.001135315,0.001256103,0.0006345604,0.0006234809],"category_scores_gemma":[0.02222093,0.000328433,0.0005577668,0.001729564,0.0004239484,0.001260198,0.0008683926,0.001035357,0.0005296908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005305714,"about_ca_system_score_gemma":0.000659874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002878857,"about_ca_topic_score_gemma":0.002157216,"domain_scores_codex":[0.9947577,0.001945763,0.0004008319,0.0008661965,0.001758566,0.0002708628],"domain_scores_gemma":[0.9764414,0.01168504,0.004612223,0.002931657,0.003849699,0.0004799126],"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":[0.0006147163,0.0004365695,0.2292275,0.0002310048,0.0002182927,0.0007019176,0.0004716621,0.1324627,0.04910176,0.003121104,0.002953484,0.5804594],"study_design_scores_gemma":[0.000009377711,0.0001124172,0.01444648,0.00001644885,0.0000213828,0.0003209793,0.00007030769,0.9715434,0.01071063,0.002130572,0.0005951651,0.00002279659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3505346,0.0003709187,0.6422047,0.0002611843,0.00005332326,0.0001689285,0.0004027417,0.00456121,0.001442355],"genre_scores_gemma":[0.9177569,0.00007165835,0.08122259,0.00003370852,0.00002100528,0.00007256972,0.0004253834,0.00009236484,0.0003037977],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003303206,"threshold_uncertainty_score":0.01746923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02938539619239948,"score_gpt":0.2524789475198476,"score_spread":0.2230935513274481,"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."}}