{"id":"W2085597081","doi":"10.1109/tse.2013.27","title":"The Impact of Classifier Configuration and Classifier Combination on Bug Localization","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Classifier (UML); Computer science; Artificial intelligence; Machine learning; Random subspace method; Source code; Probabilistic classification; Quadratic classifier; Data mining; Pattern recognition (psychology); Support vector machine; Naive Bayes classifier; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002055463,0.0001936717,0.0001514196,0.000258297,0.0001936297,0.0001944803,0.0002940924,0.0001080905,0.00002341654],"category_scores_gemma":[0.0001751112,0.0001503227,0.00008908448,0.0005022263,0.00004748774,0.000490804,0.000003806494,0.0003050206,0.00003505795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001641934,"about_ca_system_score_gemma":0.00005120296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006160021,"about_ca_topic_score_gemma":0.000001654448,"domain_scores_codex":[0.9987796,0.00003965375,0.0002630791,0.0002699908,0.0003662578,0.0002814173],"domain_scores_gemma":[0.9979993,0.001193027,0.00005754635,0.0004376792,0.0002025626,0.0001099092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001775993,0.0001399805,0.000955015,0.00005096333,0.0001081228,0.000001964866,0.0003616155,0.9313524,0.002893322,0.001680785,0.0006167897,0.06182123],"study_design_scores_gemma":[0.000556172,0.0004472799,0.05051456,0.00009630232,0.000008402156,0.00001016199,0.00001646609,0.9273739,0.02036641,0.0001582965,0.0001666871,0.0002853301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06983369,0.00003368139,0.9289669,0.00007892217,0.0004000154,0.0003236694,0.000003667607,0.0003384942,0.00002091783],"genre_scores_gemma":[0.9970604,0.00003636212,0.002597961,0.00001349746,0.00002057775,0.0001048908,0.000002037114,0.00002671851,0.0001375605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9272267,"threshold_uncertainty_score":0.6129982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01391528330065162,"score_gpt":0.2482150025998901,"score_spread":0.2342997192992385,"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."}}