{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002675482,0.001494858,0.001298812,0.008281833,0.0006804292,0.001245254,0.001928968,0.001701442,0.001340394],"category_scores_gemma":[0.01037817,0.000361568,0.001097038,0.002693023,0.0005742216,0.002544923,0.002341325,0.002411786,0.001273105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009933676,"about_ca_system_score_gemma":0.001210968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004506651,"about_ca_topic_score_gemma":0.007048667,"domain_scores_codex":[0.9968094,0.0003048264,0.0002121485,0.00110811,0.001263945,0.0003015209],"domain_scores_gemma":[0.9939466,0.002127179,0.0009897514,0.001001577,0.001577695,0.0003573194],"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":[0.0007344626,0.0006798523,0.1288939,0.0006094997,0.0005197077,0.001105731,0.0003795086,0.07732664,0.02840939,0.003213713,0.04269038,0.7154374],"study_design_scores_gemma":[0.00003486246,0.0002858284,0.02682981,0.00009263248,0.0001546226,0.001109732,0.0001923223,0.932365,0.02323225,0.004829755,0.01080012,0.00007305984],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4239114,0.006564897,0.5073724,0.001556308,0.001063503,0.0005183207,0.007288069,0.04458587,0.007139233],"genre_scores_gemma":[0.8544989,0.0007418985,0.1302405,0.0003699743,0.0001642248,0.0001907835,0.008783074,0.0004604186,0.004550248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008281833,"threshold_uncertainty_score":0.01414949,"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."}}