{"id":"W4384302745","doi":"10.1109/icse48619.2023.00214","title":"CoLeFunDa: Explainable Silent Vulnerability Fix Identification","year":2023,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Huawei Technologies (Canada)","funders":"National Key Research and Development Program of China","keywords":"Leverage (statistics); Computer science; Vulnerability (computing); Exploit; Identification (biology); Computer security; Function (biology); Vulnerability assessment; Commit; Artificial intelligence; Psychology","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":[0.002265784,0.002022645,0.000932736,0.002230031,0.000555133,0.001184968,0.003457139,0.002798005,0.005154029],"category_scores_gemma":[0.0140796,0.0006541681,0.001513079,0.0006819863,0.001365114,0.00357945,0.003179345,0.003509445,0.001723706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001171097,"about_ca_system_score_gemma":0.001864415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004975707,"about_ca_topic_score_gemma":0.01324881,"domain_scores_codex":[0.9983521,0.0004083214,0.00007564887,0.0006460962,0.0003762706,0.0001415975],"domain_scores_gemma":[0.9941288,0.003511632,0.0005023957,0.001171541,0.0005332879,0.0001523815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007530811,0.000557903,0.03104773,0.000883879,0.0002704636,0.001181983,0.001324208,0.1087288,0.02145247,0.0146907,0.0403585,0.7787504],"study_design_scores_gemma":[0.00005694855,0.0001979941,0.003884059,0.0001086877,0.00005972216,0.0004816116,0.0001602286,0.9412424,0.01583336,0.02062081,0.01727871,0.00007560512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07266641,0.001675802,0.8428436,0.001268223,0.00014503,0.0004091532,0.005068782,0.07261162,0.003311453],"genre_scores_gemma":[0.4755232,0.000479462,0.5039536,0.0008063348,0.0000926338,0.0005906709,0.01030227,0.001554344,0.00669738],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005154029,"threshold_uncertainty_score":0.01724201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03163195279413376,"score_gpt":0.3007841687061718,"score_spread":0.269152215912038,"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."}}