{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006905627,0.000153473,0.0001979436,0.000195304,0.0001701378,0.00006352232,0.000857748,0.00007147386,0.00007223216],"category_scores_gemma":[0.0007246227,0.0001450305,0.00007480624,0.0007793027,0.00006942989,0.0004048738,0.0003183654,0.0002048962,0.0001935927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006988757,"about_ca_system_score_gemma":0.0001025271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001064089,"about_ca_topic_score_gemma":3.19586e-7,"domain_scores_codex":[0.9979592,0.00005642195,0.0002818202,0.0003781394,0.0008051944,0.0005192004],"domain_scores_gemma":[0.9985411,0.0006379005,0.00006563282,0.0004966316,0.0001264419,0.0001322894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002559956,0.0006006935,0.4342541,0.0002171007,0.0002723178,0.0001505915,0.01002819,0.007381745,0.1047537,0.0008055758,0.02238504,0.4191254],"study_design_scores_gemma":[0.001974557,0.001304678,0.0972221,0.0002935583,0.00002515524,0.0002009286,0.0001470538,0.7562792,0.1345999,0.0002022175,0.005947199,0.001803392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8104321,0.0001332574,0.1880077,0.00009636052,0.0001753708,0.0002548819,7.13059e-7,0.0005211415,0.0003784604],"genre_scores_gemma":[0.7496533,0.000003814986,0.2477245,0.00001122819,0.00004309241,0.00002769673,0.000001827268,0.00002241682,0.002512083],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7488975,"threshold_uncertainty_score":0.5914173,"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."}}