{"id":"W4226069469","doi":"10.1109/qrs54544.2021.00012","title":"Analyzing Structural Security Posture to Evaluate System Design Decisions","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 21st International Conference on Software Quality, Reliability and Security (QRS)","topic":"Software Engineering Research","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leverage (statistics); Computer science; Computer security; Software security assurance; Security testing; Computer security model; Identification (biology); Resource (disambiguation); Security information and event management; Secure coding; Security service; Security through obscurity; Software; Cloud computing security; Risk analysis (engineering); Information security; Artificial intelligence; Cloud computing; Business","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01090203,0.001618577,0.000876638,0.009062931,0.0008065822,0.003188225,0.0007182516,0.001180025,0.001917376],"category_scores_gemma":[0.04715374,0.0004434963,0.0007907893,0.003383452,0.001248696,0.003326488,0.001420954,0.001104646,0.0004650397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002620702,"about_ca_system_score_gemma":0.001870461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003312272,"about_ca_topic_score_gemma":0.005228619,"domain_scores_codex":[0.9904324,0.003776015,0.0009360739,0.000621371,0.003714245,0.0005199062],"domain_scores_gemma":[0.9548638,0.02068142,0.009416785,0.003744851,0.01037094,0.0009221224],"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":[0.0007267113,0.001108033,0.239287,0.0007567334,0.0005206311,0.0005851386,0.006030521,0.1937948,0.05991667,0.04653995,0.003270259,0.4474636],"study_design_scores_gemma":[0.00008756667,0.002469682,0.1128974,0.0003420033,0.0002721485,0.0002829289,0.003853171,0.8068075,0.03319557,0.0345635,0.005011033,0.0002174221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6399956,0.0003760211,0.3398387,0.0006468007,0.00005943166,0.0006778624,0.0008295805,0.00200625,0.0155697],"genre_scores_gemma":[0.8468289,0.00009760891,0.1516015,0.00004957696,0.00001280033,0.0002534866,0.0004908075,0.00008861765,0.000576576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01090203,"threshold_uncertainty_score":0.05765617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07041286786716698,"score_gpt":0.3641071605009214,"score_spread":0.2936942926337544,"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."}}