{"id":"W2150308295","doi":"10.1145/1453101.1453109","title":"Finding programming errors earlier by evaluating runtime monitors ahead-of-time","year":2008,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; McGill University","funders":"","keywords":"Computer science; False positive paradox; Heap (data structure); Static analysis; Runtime verification; Debugging; Programming language; Benchmark (surveying); Alias; Set (abstract data type); Suite; Aliasing; Source code; Filter (signal processing); Formal verification; 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":[0.004507312,0.001758482,0.0008286712,0.002562343,0.0004524038,0.001908943,0.001648795,0.00107511,0.001185886],"category_scores_gemma":[0.02979628,0.0007664275,0.00100844,0.001039511,0.0008681861,0.003358991,0.001190231,0.001813452,0.0004975875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001022302,"about_ca_system_score_gemma":0.001806164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002877412,"about_ca_topic_score_gemma":0.004421631,"domain_scores_codex":[0.9940743,0.001271555,0.0004660111,0.001407755,0.002292194,0.0004883168],"domain_scores_gemma":[0.9728952,0.01197126,0.005363954,0.005490345,0.003763313,0.0005159573],"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":[0.001525237,0.0008627254,0.2658753,0.0006669636,0.000546803,0.001017053,0.001497385,0.1858312,0.1096819,0.01034332,0.00531693,0.4168353],"study_design_scores_gemma":[0.00005196388,0.0006503267,0.02188784,0.00009922896,0.0002401182,0.0002784219,0.0002447157,0.8761037,0.08757091,0.008573309,0.004212946,0.00008661725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6029308,0.0006918198,0.3680953,0.0005891678,0.000166086,0.0002035106,0.0008009212,0.02324724,0.003275203],"genre_scores_gemma":[0.851754,0.0001349316,0.14516,0.0001377653,0.00003328157,0.00008128111,0.0008036873,0.0008213929,0.001073639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004507312,"threshold_uncertainty_score":0.02383727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04246659249662417,"score_gpt":0.3194692976652465,"score_spread":0.2770027051686223,"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."}}