{"id":"W4289730357","doi":"10.48550/arxiv.1808.00594","title":"Improving IR-Based Bug Localization with Context-Aware Query\\n Reformulation","year":2018,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Information retrieval; Context (archaeology); Query expansion; Baseline (sea); State (computer science); Query language; Data mining; Natural language processing; Programming language","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":[0.002088886,0.001975764,0.002130111,0.005889326,0.000833365,0.00149495,0.002288716,0.001380292,0.003573204],"category_scores_gemma":[0.01072426,0.000501374,0.001584137,0.003305901,0.0008603582,0.003726477,0.002133684,0.001393301,0.002703331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009621026,"about_ca_system_score_gemma":0.001967206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01018815,"about_ca_topic_score_gemma":0.008350782,"domain_scores_codex":[0.9967673,0.0007276654,0.0003210284,0.0008110142,0.001150129,0.0002228354],"domain_scores_gemma":[0.9932407,0.00260956,0.0008709401,0.00139732,0.001725337,0.0001560124],"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":[0.0003908604,0.0004733829,0.006272988,0.001064622,0.0001357182,0.0004977084,0.001132733,0.01250524,0.1041408,0.003579688,0.02746134,0.8423449],"study_design_scores_gemma":[0.0003725792,0.001556436,0.01429269,0.0002051983,0.0009841057,0.003388894,0.001762021,0.7068663,0.2043977,0.01097449,0.05483818,0.0003614337],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1263855,0.007451361,0.7737613,0.001484861,0.000320185,0.0005910219,0.001845766,0.08329123,0.004868752],"genre_scores_gemma":[0.3506828,0.00162408,0.6332888,0.0009126648,0.0003102514,0.0002365108,0.005676731,0.001684829,0.005583285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01018815,"threshold_uncertainty_score":0.02025771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05079169819544985,"score_gpt":0.1920231809668719,"score_spread":0.1412314827714221,"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."}}