{"id":"W3011074760","doi":"10.1109/mtv48867.2019.00009","title":"Expediting Design Bug Discovery in Regressions of x86 Processors Using Machine Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Debugging; Computer science; Expediting; x86; Leverage (statistics); Overhead (engineering); Embedded system; Software bug; Operating system; Machine learning; Engineering; Software","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":[0.0006821602,0.0006834375,0.0003723996,0.0009936715,0.0002048963,0.0003060872,0.0006974994,0.0004534625,0.001221687],"category_scores_gemma":[0.005275048,0.0002611913,0.0004075026,0.0003898506,0.0002407335,0.0006177321,0.0003599124,0.0005503944,0.0003164149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003283121,"about_ca_system_score_gemma":0.0006705965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002754904,"about_ca_topic_score_gemma":0.00404662,"domain_scores_codex":[0.9995963,0.0001310145,0.00002487788,0.0001034798,0.0001048734,0.00003944228],"domain_scores_gemma":[0.9958527,0.002842145,0.0005891884,0.0003292432,0.0003353155,0.00005131898],"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":[0.0005316434,0.0005361029,0.05338907,0.0002135192,0.000147135,0.0004329803,0.000195838,0.3623499,0.08985569,0.001053579,0.002131999,0.4891626],"study_design_scores_gemma":[0.000009586219,0.0001271622,0.003304225,0.000006304303,0.00001102029,0.0000613959,0.00001391441,0.9777777,0.01800086,0.0003559648,0.000324933,0.000006813028],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6440842,0.0004251883,0.3330164,0.0002021474,0.00002450975,0.0001311418,0.000292107,0.02041305,0.001411283],"genre_scores_gemma":[0.8292824,0.00007454304,0.1690829,0.00005809859,0.000006423048,0.00005032442,0.0003363255,0.0001997053,0.0009092488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002754904,"threshold_uncertainty_score":0.005477786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04276156810023777,"score_gpt":0.2631641248513293,"score_spread":0.2204025567510915,"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."}}