{"id":"W4287064711","doi":"10.48550/arxiv.2107.13708","title":"Learning how to listen: Automatically finding bug patterns in event-driven JavaScript APIs","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Software Engineering Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"JavaScript; Computer science; Event (particle physics); Set (abstract data type); Code (set theory); Ajax; Java; Source code; Programming language; Artificial intelligence; Data mining; Natural language processing; Web service","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001809046,0.00185264,0.0008501172,0.004058146,0.0004408234,0.001233061,0.001866896,0.001383084,0.0005214514],"category_scores_gemma":[0.01402497,0.0006331804,0.00120729,0.001928888,0.0006507793,0.002969932,0.001391593,0.001323987,0.0008903769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004821908,"about_ca_system_score_gemma":0.00109085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004019892,"about_ca_topic_score_gemma":0.006763966,"domain_scores_codex":[0.9973776,0.0004284603,0.0002721982,0.001168124,0.0005838703,0.0001697188],"domain_scores_gemma":[0.9886952,0.006677187,0.001816895,0.001146061,0.001302392,0.0003623397],"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":[0.0005699023,0.0009722285,0.22583,0.001041817,0.0003263414,0.001661988,0.002342449,0.02277417,0.03425055,0.00143527,0.02027034,0.688525],"study_design_scores_gemma":[0.0001500321,0.0003907988,0.06068198,0.0001395543,0.0002479574,0.001408284,0.001087948,0.8808793,0.03606074,0.007566475,0.01125951,0.0001274958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6065153,0.001464323,0.3350265,0.0007382823,0.0001355709,0.0003873087,0.005708669,0.04844021,0.001583915],"genre_scores_gemma":[0.6871973,0.0003898045,0.2970721,0.0002692804,0.0000625028,0.0002759573,0.01154593,0.001281274,0.001905748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004058146,"threshold_uncertainty_score":0.009567261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06200842605263655,"score_gpt":0.2109301426750954,"score_spread":0.1489217166224588,"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."}}