{"id":"W2148965601","doi":"10.1109/wcre.2011.46","title":"Make it or Break it: Mining Anomalies from Linux Kbuild","year":2011,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Linux kernel; Computer science; Kernel (algebra); Operating system; Source code; Consistency (knowledge bases); Configfs; System call; Anomaly detection; Code (set theory); Software bug; sysfs; Data mining; Programming language; Software; Artificial intelligence; Set (abstract data type)","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002099715,0.0001330068,0.00013945,0.0001170078,0.00005896269,0.0001373506,0.001220685,0.00007001667,0.001106319],"category_scores_gemma":[0.000344751,0.0001044346,0.00004226416,0.0003348992,0.00003578893,0.0002584343,0.0005215966,0.0001302996,0.0003980965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002890918,"about_ca_system_score_gemma":0.00008049222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005685701,"about_ca_topic_score_gemma":0.0002369873,"domain_scores_codex":[0.9987212,0.0000249973,0.0001811712,0.0003833419,0.0003194796,0.0003698207],"domain_scores_gemma":[0.9983748,0.0006844368,0.00002541364,0.0007162723,0.00006389659,0.0001351423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002216133,0.0007076311,0.2860661,0.0001551362,0.0006305132,0.002134152,0.06348241,0.0003743439,0.002506772,0.02479116,0.2737153,0.3452148],"study_design_scores_gemma":[0.002338034,0.001193948,0.6746233,0.0003257738,0.0000393139,0.0002157279,0.001189039,0.1216628,0.03576204,0.003025771,0.1570775,0.00254673],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2934105,0.0001313278,0.6782743,0.001998461,0.0009019948,0.0001971307,0.000007981102,0.001397096,0.02368117],"genre_scores_gemma":[0.6274479,0.00000732807,0.3640727,0.0006744895,0.0001082724,0.00001671883,0.000001593676,0.00001867294,0.007652254],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3885572,"threshold_uncertainty_score":0.9998068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07296652176832846,"score_gpt":0.2807233845586774,"score_spread":0.2077568627903489,"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."}}