{"id":"W4312437398","doi":"10.1145/3524610.3527872","title":"Deep API learning revisited","year":2022,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Python (programming language); Java; Documentation; Encoder; Source code; Architecture; Artificial intelligence; Deep learning; Preprocessor; Task (project management); Programming language; Machine learning; Information retrieval; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003534823,0.00004325023,0.00005111736,0.0001009023,0.0001742515,0.00006618296,0.0006924323,0.000008099922,0.0005858549],"category_scores_gemma":[0.0002927873,0.00004409907,0.00002327802,0.00060771,0.000005332362,0.0001048487,0.0008015516,0.0002730829,0.0001174876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005476994,"about_ca_system_score_gemma":0.00001920977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008190876,"about_ca_topic_score_gemma":1.499416e-7,"domain_scores_codex":[0.9991679,0.0000619051,0.0000645119,0.0001844643,0.0003344292,0.0001868343],"domain_scores_gemma":[0.9993564,0.0002808384,0.00001056881,0.0002808967,0.00002251468,0.00004873178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000101087,0.0001477534,0.09867897,0.00006400621,0.00006424588,0.0003727665,0.003091792,0.358873,0.002530005,0.2032926,0.02642829,0.3064464],"study_design_scores_gemma":[0.0001801052,0.0001210845,0.01091444,0.000002477551,8.496406e-7,0.00004726128,0.00004808597,0.8542104,0.0002724527,0.0003882279,0.1336319,0.0001827513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007106556,0.0001601422,0.9886256,0.0004248337,0.0001207654,0.00005871044,7.177461e-8,0.000816073,0.002687291],"genre_scores_gemma":[0.962236,0.000003057193,0.03344942,0.0001546295,0.00002631912,0.00002732606,0.000001533167,0.000008349863,0.004093318],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9551761,"threshold_uncertainty_score":0.64147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01343866028717355,"score_gpt":0.2508100375218349,"score_spread":0.2373713772346613,"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."}}