{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009836977,0.00006869198,0.00007254098,0.0001460989,0.0001157202,0.0001943197,0.0006985642,0.0000346056,0.00007240773],"category_scores_gemma":[0.0008839752,0.00006562706,0.00003219108,0.001143734,0.00002099876,0.0003622478,0.0003108112,0.0001038331,0.001710062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009611589,"about_ca_system_score_gemma":0.00005329744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004651718,"about_ca_topic_score_gemma":0.000003860308,"domain_scores_codex":[0.9987851,0.0000440634,0.0001528794,0.0003400051,0.0003728145,0.0003051371],"domain_scores_gemma":[0.9984692,0.00057938,0.00001736975,0.0007509546,0.0001000016,0.00008306209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00001807159,0.0005354386,0.1194436,0.0005568558,0.00009662016,0.0001909357,0.00475666,0.03806024,0.03765446,0.244776,0.379782,0.1741292],"study_design_scores_gemma":[0.0003264313,0.00007212408,0.5844117,0.00001458122,0.000002076473,0.00000745228,0.0001050303,0.3421626,0.03744396,0.007849412,0.02723439,0.0003702571],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3027795,0.00001958172,0.6922755,0.001378727,0.0005332195,0.0002312545,0.000001275048,0.002191582,0.0005893633],"genre_scores_gemma":[0.987282,0.000005472596,0.003766066,0.00003164999,0.0000342333,0.00009199484,0.000004606378,0.00000723191,0.008776757],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6885095,"threshold_uncertainty_score":0.9990672,"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."}}