{"id":"W4384434405","doi":"10.2139/ssrn.4511776","title":"Design and Implementation of the Vizapi Tool for Visualizing Java Static and Dynamic Analysis Results","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Java; Computer science; Static analysis; Programming language; Software engineering","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":[0.002705707,0.00225436,0.001498617,0.003119553,0.0007716409,0.004283302,0.005424982,0.001364576,0.03149534],"category_scores_gemma":[0.008089631,0.001861763,0.001698383,0.001959368,0.0009866031,0.003035606,0.002538416,0.002839996,0.01213797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009138171,"about_ca_system_score_gemma":0.002151098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004326557,"about_ca_topic_score_gemma":0.003201582,"domain_scores_codex":[0.9983612,0.0002087403,0.0001814258,0.0004417564,0.000616612,0.0001903162],"domain_scores_gemma":[0.995885,0.001354927,0.0002675276,0.00109979,0.001079416,0.0003133967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002920987,0.0008678318,0.008966252,0.002233919,0.0007017956,0.001141711,0.002030143,0.02171375,0.1092137,0.03687451,0.1923956,0.6209397],"study_design_scores_gemma":[0.001477941,0.0005459939,0.00829496,0.0004893947,0.0005011729,0.0009975632,0.0004571692,0.4605777,0.2355811,0.03857173,0.2519486,0.0005567599],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004687584,0.000173011,0.6724043,0.0001970962,0.0001363221,0.0004133326,0.00215399,0.3152386,0.004595842],"genre_scores_gemma":[0.1254695,0.0004873903,0.7498149,0.0007810246,0.000161476,0.001934677,0.01014546,0.09637996,0.01482556],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03149534,"threshold_uncertainty_score":0.1053624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02134454425276151,"score_gpt":0.3377937278145316,"score_spread":0.3164491835617701,"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."}}