{"id":"W4402516515","doi":"10.1145/3695995","title":"Deep API Sequence Generation via Golden Solution Samples and API Seeds","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Sequence (biology); Programming language; Biology","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":[0.001349064,0.001834915,0.001431658,0.002234122,0.0006996028,0.001085577,0.002212111,0.001552481,0.00388759],"category_scores_gemma":[0.009938694,0.0007640226,0.001431423,0.001448239,0.0007268334,0.002408704,0.001314825,0.001554919,0.00224224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008620215,"about_ca_system_score_gemma":0.002331634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008114816,"about_ca_topic_score_gemma":0.01682202,"domain_scores_codex":[0.9986348,0.0002857249,0.00009391275,0.0004438809,0.0003878509,0.0001538457],"domain_scores_gemma":[0.9968113,0.001392426,0.0002152847,0.0006619907,0.0007361301,0.0001827338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009223768,0.0009298961,0.01565226,0.0007312198,0.0001751786,0.0007519417,0.0003880062,0.1594542,0.02619131,0.008510452,0.03780685,0.7484865],"study_design_scores_gemma":[0.0001044522,0.0001848914,0.0007751497,0.00002587933,0.0000576816,0.0001715222,0.0001000775,0.9814388,0.008418509,0.005144745,0.003557611,0.00002057858],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2422707,0.002272755,0.7100698,0.0009144588,0.0003032501,0.000604444,0.001916925,0.03562713,0.006020565],"genre_scores_gemma":[0.4911722,0.0003654155,0.4928754,0.0005081503,0.0000768001,0.000480016,0.008389559,0.001387859,0.004744589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008114816,"threshold_uncertainty_score":0.01613516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1355813693896089,"score_gpt":0.316032012399771,"score_spread":0.1804506430101621,"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."}}