{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001858647,0.0009716155,0.0008225604,0.008965604,0.0009278888,0.001596333,0.001594016,0.00112041,0.0003494388],"category_scores_gemma":[0.014392,0.0004574347,0.0008200825,0.006034632,0.0009392948,0.002061088,0.001536764,0.001083819,0.0004527714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001118718,"about_ca_system_score_gemma":0.001283744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01597157,"about_ca_topic_score_gemma":0.01632992,"domain_scores_codex":[0.9955763,0.000337874,0.0005967484,0.0009995895,0.002083691,0.0004058748],"domain_scores_gemma":[0.9842588,0.006614242,0.003786247,0.001949438,0.002877248,0.0005140718],"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":[0.0005850184,0.0005531189,0.742424,0.000931738,0.0003018346,0.004958046,0.003755192,0.01714809,0.01485361,0.002468304,0.01756978,0.1944513],"study_design_scores_gemma":[0.00008498802,0.0003739637,0.5392723,0.0003646715,0.0004459059,0.006110344,0.006123887,0.3573063,0.04191478,0.01118103,0.036548,0.0002738355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9431947,0.00143676,0.0335784,0.0005149042,0.0001377518,0.0002110301,0.01103936,0.008145596,0.001741564],"genre_scores_gemma":[0.9214653,0.000433984,0.05243563,0.0001284859,0.00005346342,0.0001589015,0.02398917,0.0005264273,0.0008086929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01597157,"threshold_uncertainty_score":0.03175724,"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."}}