{"id":"W4400242114","doi":"10.1145/3643991.3644920","title":"Enhancing Performance Bug Prediction Using Performance Code Metrics","year":2024,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of Toronto; IBM (Canada)","funders":"","keywords":"Computer science; Software bug; Code (set theory); Programming language; Performance prediction; 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":[0.003495648,0.002192998,0.001023429,0.005580915,0.0003346294,0.001487197,0.0009428001,0.0009601516,0.000657481],"category_scores_gemma":[0.02342481,0.0004700065,0.0007970284,0.002595867,0.0003972828,0.002391316,0.0009590852,0.001449523,0.001029623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006436669,"about_ca_system_score_gemma":0.001503889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008813991,"about_ca_topic_score_gemma":0.00878902,"domain_scores_codex":[0.9970788,0.0005362195,0.0002333157,0.0005877495,0.001235692,0.0003283029],"domain_scores_gemma":[0.9781502,0.008243863,0.004893524,0.001648354,0.00625204,0.0008120927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002481861,0.0006204232,0.5921922,0.0003647415,0.0001935461,0.0003546508,0.0002672191,0.1064523,0.006943598,0.001222241,0.01369946,0.2774415],"study_design_scores_gemma":[0.00001652149,0.0001996782,0.06111889,0.00007121662,0.0000598268,0.0001933345,0.00007501765,0.9272098,0.006562579,0.001587104,0.002853133,0.00005292743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.720529,0.003188789,0.2432453,0.001436315,0.0002284906,0.0002400551,0.004098804,0.02250933,0.004523853],"genre_scores_gemma":[0.9420192,0.0004277707,0.05100687,0.00017572,0.00006997286,0.00008727062,0.004568582,0.0004058704,0.001238879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008813991,"threshold_uncertainty_score":0.01848698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02942112156448311,"score_gpt":0.2746400778753205,"score_spread":0.2452189563108373,"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."}}