{"id":"W2614734074","doi":"10.1109/icst.2017.17","title":"Perphecy: Performance Regression Test Selection Made Simple but Effective","year":2017,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Regression testing; Computer science; Commit; Generality; Regression; Simple (philosophy); Selection (genetic algorithm); Regression analysis; Machine learning; Software; Statistics; Software system; Mathematics; Operating system","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":[0.01200302,0.00177507,0.001823286,0.001975592,0.0009406319,0.002595635,0.003533683,0.00213044,0.005447479],"category_scores_gemma":[0.06985947,0.0009197692,0.0006838425,0.0009269896,0.0017386,0.004855364,0.002820639,0.003338027,0.002895025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008631093,"about_ca_system_score_gemma":0.004107781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002176489,"about_ca_topic_score_gemma":0.003264391,"domain_scores_codex":[0.985828,0.005885423,0.0006708019,0.002503648,0.004074102,0.001037992],"domain_scores_gemma":[0.9463223,0.02689176,0.002959319,0.01401088,0.008845682,0.0009700426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00177604,0.0007904449,0.03877853,0.0006409678,0.0002190371,0.001308174,0.0006046522,0.02055433,0.06319505,0.02760363,0.05040489,0.7941243],"study_design_scores_gemma":[0.000914598,0.002020117,0.02096071,0.0003919488,0.0004306966,0.00344553,0.0003802677,0.6810982,0.1705758,0.05269994,0.06671051,0.0003716585],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08009491,0.0007712505,0.8658592,0.003177911,0.0007853779,0.0006221564,0.0005943359,0.03764088,0.01045398],"genre_scores_gemma":[0.5323508,0.0002464687,0.447855,0.001914019,0.000466135,0.0005473718,0.001175403,0.004479394,0.01096544],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01200302,"threshold_uncertainty_score":0.06347877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009765202068299257,"score_gpt":0.2605345804974529,"score_spread":0.2507693784291536,"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."}}