{"id":"W4381304075","doi":"10.1109/tse.2023.3281275","title":"Multi-Granularity Detector for Vulnerability Fixes","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Research Foundation Singapore; National University of Singapore","keywords":"Computer science; Commit; Granularity; Vulnerability (computing); Source code; Software; Python (programming language); Code (set theory); Data mining; Computer security; Database; Operating system; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005596007,0.0002798999,0.0002535902,0.0005039109,0.0002438035,0.0001314476,0.0007575448,0.0001496082,0.00001373396],"category_scores_gemma":[0.0006898623,0.0003055919,0.0002294853,0.001304088,0.00002698031,0.0003663678,0.000009101852,0.0004678154,0.0001152714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001646289,"about_ca_system_score_gemma":0.00006090445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001821656,"about_ca_topic_score_gemma":0.000007424411,"domain_scores_codex":[0.9980123,0.00002937225,0.0002780759,0.000616424,0.0003883279,0.0006754734],"domain_scores_gemma":[0.9965098,0.00229674,0.00002550398,0.0008331732,0.0001260979,0.0002086947],"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":[0.00002025012,0.0001657713,0.0007087398,0.0003399724,0.00009684834,0.0000227957,0.0003904433,0.9594986,0.006249525,0.00007111499,0.0003041016,0.03213183],"study_design_scores_gemma":[0.001015229,0.0001671961,0.01419268,0.00007352116,0.00001494215,0.00001302767,0.000007845957,0.9166115,0.06543197,0.00004558631,0.001788403,0.0006380979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0499103,0.00002825645,0.9427131,0.00006061838,0.001603508,0.0004793509,0.00005876861,0.005145562,5.140345e-7],"genre_scores_gemma":[0.7082132,0.000008865254,0.2909662,0.00002084746,0.00005918285,0.0004966644,0.000003710532,0.00005837795,0.0001728987],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6583029,"threshold_uncertainty_score":0.9999396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03505384984562248,"score_gpt":0.2861512386088713,"score_spread":0.2510973887632488,"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."}}