{"id":"W2406365535","doi":"10.1109/saner.2016.80","title":"RACK: Automatic API Recommendation Using Crowdsourced Knowledge","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":151,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Java; Code (set theory); Information retrieval; Matching (statistics); Precision and recall; Programming language; Search engine; Database; Set (abstract data type)","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":[0.003468363,0.002706124,0.00210411,0.01092997,0.001486986,0.001760383,0.003523005,0.002338176,0.005332583],"category_scores_gemma":[0.01709832,0.0007986436,0.001722613,0.006735601,0.0007684071,0.003617581,0.00307648,0.001544492,0.006121145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001426165,"about_ca_system_score_gemma":0.003888632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02983901,"about_ca_topic_score_gemma":0.0480383,"domain_scores_codex":[0.9944883,0.001133483,0.000384665,0.00194337,0.001755546,0.0002946992],"domain_scores_gemma":[0.9894119,0.005384484,0.000765385,0.002304805,0.001756194,0.0003772078],"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":[0.001385104,0.001175369,0.01553264,0.002592052,0.0006959414,0.0009569391,0.001401508,0.03188952,0.02661422,0.004427087,0.1480165,0.7653131],"study_design_scores_gemma":[0.0004625983,0.0003626546,0.009236297,0.0003014211,0.0003235439,0.0006265843,0.00163971,0.8698596,0.02539977,0.02417163,0.06727451,0.000341736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09063884,0.004419764,0.7359067,0.002169121,0.0006449706,0.003022664,0.03760748,0.1062249,0.01936562],"genre_scores_gemma":[0.2873628,0.0009041621,0.6633062,0.0007744239,0.0002251402,0.001249823,0.03625303,0.001549372,0.008374909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02983901,"threshold_uncertainty_score":0.05933064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05570094367063218,"score_gpt":0.3379473080884403,"score_spread":0.2822463644178081,"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."}}