{"id":"W2591557490","doi":"","title":"Predicting Method Crashes with Bytecode Operations","year":2013,"lang":"en","type":"article","venue":"ACM International Conference Proceeding Series","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"AspectJ; Crash; Computer science; Bytecode; Overhead (engineering); Software; Eclipse; Real-time computing; Machine learning; Data mining; Operating system; Aspect-oriented programming; Java","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002077873,0.0001469875,0.0001147444,0.0001614761,0.0001592229,0.001136547,0.00182854,0.00004010372,0.0002418989],"category_scores_gemma":[0.001435879,0.000124057,0.0000228688,0.0002610345,0.00005431936,0.002904547,0.0005477222,0.0002135974,0.00007800405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004977578,"about_ca_system_score_gemma":0.0001401347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002138704,"about_ca_topic_score_gemma":0.00002687464,"domain_scores_codex":[0.9987046,0.00001320574,0.0001851753,0.0003597486,0.0004774546,0.0002598187],"domain_scores_gemma":[0.9983338,0.0002436987,0.00004045308,0.0003029488,0.0009892831,0.00008979212],"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":[0.00001601031,0.00008193941,0.4999118,0.00006630515,0.000247771,0.000009955345,0.00582837,0.002402211,0.02372835,0.4447234,0.00130518,0.02167875],"study_design_scores_gemma":[0.0008329921,0.0004930948,0.1743871,0.0004104464,0.00001784852,0.0003632143,0.002618079,0.7101956,0.06133373,0.04606112,0.00214359,0.001143168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.231231,0.00001468724,0.7556248,0.008869934,0.0003400104,0.0002701689,0.000003716003,0.0006048745,0.003040824],"genre_scores_gemma":[0.672728,0.000006121508,0.3260568,0.00005234129,0.00008577899,0.0001753043,0.000004115515,0.000009919114,0.0008815826],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7077934,"threshold_uncertainty_score":0.9999003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03894688473146694,"score_gpt":0.309919357174983,"score_spread":0.270972472443516,"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."}}