{"id":"W4400647844","doi":"10.1016/j.cose.2024.103994","title":"SCL-CVD: Supervised contrastive learning for code vulnerability detection via GraphCodeBERT","year":2024,"lang":"en","type":"article","venue":"Computers & Security","topic":"Software Engineering Research","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Vulnerability (computing); Code (set theory); Artificial intelligence; Computer security; 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":[0.001037526,0.0002613013,0.0002847158,0.0002652114,0.0002730238,0.0004886792,0.0008227458,0.0001301665,0.000007249529],"category_scores_gemma":[0.0005257294,0.0002654397,0.0002166548,0.0008082674,0.00009293089,0.0005764826,0.0003198944,0.0006800435,0.00003291217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002339173,"about_ca_system_score_gemma":0.0001144167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008590385,"about_ca_topic_score_gemma":0.00003521312,"domain_scores_codex":[0.9976298,0.00019834,0.0002923633,0.0009007396,0.000393957,0.0005848478],"domain_scores_gemma":[0.9960163,0.003019337,0.00003288043,0.0005063238,0.0002185816,0.0002065433],"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":[0.0002341091,0.0005739831,0.0097758,0.002197822,0.0007156884,0.0002186168,0.01647393,0.02388941,0.01723118,0.01726804,0.00512855,0.9062929],"study_design_scores_gemma":[0.0004326958,0.000240639,0.006711882,0.00007972636,0.0000113327,0.00002857288,0.000009865686,0.9752111,0.004572258,0.005907644,0.006473619,0.0003206526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1302112,0.0004818246,0.8648963,0.000337716,0.001998746,0.0005061787,0.000007355517,0.001535389,0.00002529873],"genre_scores_gemma":[0.9859835,0.00001090429,0.01351876,0.00006908082,0.0002487597,0.0001043107,0.00001103885,0.0000311319,0.0000225143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9513217,"threshold_uncertainty_score":0.9999798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0147640847943746,"score_gpt":0.2703110922352167,"score_spread":0.2555470074408421,"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."}}