{"id":"W2767269462","doi":"10.1109/icsme.2017.13","title":"An Exploratory Study of Performance Regression Introducing Code Changes","year":2017,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Regression testing; Performance metric; Software quality; Benchmark (surveying); Commit; Performance prediction; Reliability engineering; Software performance testing; Quality (philosophy); Software regression; Metric (unit); Software bug; Software; Regression analysis; Performance indicator; Software metric; Code (set theory); Regression; Machine learning; Software development; Simulation; Operating system; Statistics; Database; Engineering; Software construction; Operations management","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":[0.008890179,0.0005579141,0.0004206329,0.002987636,0.0006792189,0.001123486,0.001020832,0.0006078389,0.0005947967],"category_scores_gemma":[0.07855564,0.0003926509,0.0004627948,0.002134713,0.001232901,0.001425793,0.001013089,0.001408565,0.0002636716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008445322,"about_ca_system_score_gemma":0.0008203408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001997427,"about_ca_topic_score_gemma":0.003273387,"domain_scores_codex":[0.9876921,0.00428067,0.0009691106,0.00188026,0.00456028,0.0006176278],"domain_scores_gemma":[0.7710912,0.1623884,0.0320339,0.01120318,0.02133812,0.001945239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009153333,0.00255314,0.7776113,0.001265891,0.0003016239,0.003465727,0.02814059,0.006569528,0.0394266,0.001141839,0.003537292,0.1350712],"study_design_scores_gemma":[0.00006144011,0.003281999,0.9464607,0.0001838964,0.00013301,0.001382514,0.008313961,0.01713278,0.01539753,0.0006657334,0.006882522,0.0001038579],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959754,0.0001153332,0.002441642,0.0001028547,0.000009759381,0.0001739052,0.0003780114,0.0001897146,0.0006133881],"genre_scores_gemma":[0.9911958,0.0001032391,0.006864113,0.00008480636,0.00002316625,0.0002302299,0.0009338055,0.00009190538,0.0004728581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008890179,"threshold_uncertainty_score":0.04701632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05274218923814708,"score_gpt":0.3253429721121572,"score_spread":0.2726007828740101,"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."}}