{"id":"W2761352457","doi":"10.1145/3133923","title":"IDE <sup> <i>al</i> </sup> : efficient and precise alias-aware dataflow analysis","year":2017,"lang":"en","type":"article","venue":"Proceedings of the ACM on Programming Languages","topic":"Security and Verification in Computing","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Heinz Nixdorf Stiftung","keywords":"Alias; Computer science; Dataflow; Programming language; Ideal (ethics); Aliasing; Static analysis; Parallel computing; Theoretical computer science; Algorithm; Data mining; Artificial intelligence","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":[0.003412938,0.0007868782,0.0006729862,0.00173507,0.00080209,0.003575636,0.002413542,0.0008611924,0.01623194],"category_scores_gemma":[0.00942819,0.0007002152,0.001151103,0.001092709,0.001933989,0.005305835,0.003191995,0.002552811,0.008663601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001334011,"about_ca_system_score_gemma":0.002410143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003184716,"about_ca_topic_score_gemma":0.004415594,"domain_scores_codex":[0.996258,0.0005444446,0.0002845261,0.0004408374,0.002150114,0.0003221165],"domain_scores_gemma":[0.9926674,0.001860073,0.0003911789,0.002715862,0.002105692,0.0002597765],"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":[0.0011711,0.0002861896,0.0089191,0.0006351889,0.00009647221,0.0004258608,0.000479101,0.02415611,0.04998903,0.1247634,0.2482625,0.5408161],"study_design_scores_gemma":[0.000214808,0.0002333412,0.003342469,0.0001994319,0.00009058882,0.0007212999,0.0002564608,0.4126096,0.1389705,0.0710546,0.3721441,0.0001626631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009870373,0.0003330072,0.8887612,0.001162676,0.0003569867,0.0001351659,0.001881415,0.07082032,0.02667892],"genre_scores_gemma":[0.1806014,0.0005353266,0.7748162,0.001719294,0.0005473974,0.0002467019,0.00742004,0.01668366,0.01742993],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01623194,"threshold_uncertainty_score":0.05430132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02093102351995533,"score_gpt":0.2932984388493426,"score_spread":0.2723674153293873,"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."}}