{"id":"W4405601139","doi":"10.1109/icsme58944.2024.00014","title":"An Empirical Study of Automatic Program Repair Techniques for Injection Vulnerabilities","year":2024,"lang":"en","type":"article","venue":"","topic":"Security and Verification in Computing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Empirical research; Reliability engineering; Engineering; Statistics; Mathematics","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":[0.01021979,0.0005580053,0.0003186967,0.002713375,0.0004869048,0.0009762127,0.001240775,0.0008984922,0.001219812],"category_scores_gemma":[0.1179265,0.0003842743,0.0004585497,0.001989549,0.001036994,0.002235344,0.0007547325,0.001420081,0.0004799005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008032182,"about_ca_system_score_gemma":0.0009135042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001786202,"about_ca_topic_score_gemma":0.003340196,"domain_scores_codex":[0.9877282,0.004943256,0.001118878,0.001648175,0.004091137,0.0004704322],"domain_scores_gemma":[0.6596974,0.2757227,0.0293725,0.01661952,0.01689821,0.001689749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001555505,0.00452526,0.5975113,0.002391316,0.0003739513,0.0006727299,0.006243763,0.02245018,0.01227697,0.002092137,0.006390607,0.3435163],"study_design_scores_gemma":[0.0003199543,0.00654289,0.8043471,0.0005425292,0.0003675655,0.001980829,0.005096002,0.1399726,0.01987355,0.001931123,0.01889223,0.0001336319],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928769,0.0006579316,0.004020906,0.0001426656,0.00001273853,0.000149825,0.0004387806,0.0002954429,0.001404651],"genre_scores_gemma":[0.989141,0.0003114811,0.008676306,0.00006070284,0.00001266298,0.0001299709,0.001059009,0.00008645032,0.000522512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01021979,"threshold_uncertainty_score":0.054048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04568936453882959,"score_gpt":0.3915196118229399,"score_spread":0.3458302472841103,"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."}}