{"id":"W4413279387","doi":"10.1145/3760775","title":"VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMs","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Software Engineering Research","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Vulnerability (computing); Computer security","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":[0.00205319,0.001909813,0.001214146,0.002543185,0.0004973365,0.001053042,0.002312496,0.001588388,0.002162028],"category_scores_gemma":[0.008109805,0.0005541764,0.001753321,0.001299723,0.001253958,0.003550238,0.002732311,0.002040079,0.001446656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009055794,"about_ca_system_score_gemma":0.001436277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002938593,"about_ca_topic_score_gemma":0.005435861,"domain_scores_codex":[0.9984824,0.0004361861,0.00008876874,0.000514705,0.0003496611,0.0001282621],"domain_scores_gemma":[0.9964347,0.001877266,0.0002432755,0.001000395,0.0003359636,0.000108399],"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":[0.0005849528,0.0006304093,0.01577929,0.0006263474,0.0002736127,0.0005864232,0.0005175393,0.2344157,0.02606937,0.005791798,0.03391153,0.680813],"study_design_scores_gemma":[0.00006953746,0.0002749628,0.001426558,0.00005242939,0.00009398221,0.0003332731,0.0001297261,0.9604155,0.01466218,0.0126255,0.009872499,0.00004382992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2523063,0.005084245,0.6538383,0.002171389,0.0004886457,0.0005526146,0.004973863,0.07451726,0.00606738],"genre_scores_gemma":[0.6058777,0.0007918598,0.37088,0.001686972,0.0001566097,0.0005356091,0.01344009,0.001776438,0.004854663],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002938593,"threshold_uncertainty_score":0.01085842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1202110665962654,"score_gpt":0.3391362736804353,"score_spread":0.2189252070841698,"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."}}