{"id":"W875360987","doi":"10.4230/dagrep.3.2.1","title":"Fault Prediction, Localization, and Repair (Dagstuhl Seminar 13061)","year":2013,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Software Engineering Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universität des Saarlandes; University College London; Case Western Reserve University; Institut national de recherche en informatique et en automatique (INRIA); Universidade do Porto; University of Waterloo; Oregon State University","keywords":"Debugging; Computer science; Program slicing; Program comprehension; Redundancy (engineering); Software engineering; Symbolic execution; Software bug; Software; Programming language; Machine learning; Software system; Operating system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001839376,0.002689341,0.001794798,0.001832174,0.0008906565,0.003882102,0.001165376,0.002635573,0.1414248],"category_scores_gemma":[0.00278646,0.001230661,0.001352463,0.00149924,0.0007165055,0.002869745,0.003544448,0.004390773,0.1210462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001753874,"about_ca_system_score_gemma":0.001107311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007843953,"about_ca_topic_score_gemma":0.001401038,"domain_scores_codex":[0.9986057,0.0002811944,0.00007139748,0.0003995996,0.0003407266,0.0003014714],"domain_scores_gemma":[0.9991496,0.0002406158,0.00004139505,0.0001012869,0.0001730395,0.00029402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000658474,0.0004137368,0.0002132305,0.0005434834,0.00006204227,0.0003555312,0.0001649432,0.003579138,0.007715032,0.0245022,0.6429805,0.3188117],"study_design_scores_gemma":[0.0002969728,0.000472478,0.002222994,0.0006109485,0.00005946376,0.0004358295,0.0001126679,0.00971581,0.005083768,0.05533289,0.9255715,0.00008480457],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.03362747,0.08770308,0.2027168,0.0646494,0.07868088,0.0009466953,0.01413751,0.0211185,0.4964196],"genre_scores_gemma":[0.07554366,0.03535395,0.08310582,0.004934965,0.01359765,0.0010308,0.01328043,0.007935822,0.7652169],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1414248,"threshold_uncertainty_score":0.4731131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008543873323558932,"score_gpt":0.2311270293898638,"score_spread":0.2225831560663049,"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."}}