{"id":"W2884547357","doi":"10.1145/3196398.3196459","title":"Predicting developers' IDE commands with machine learning","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Session (web analytics); Parsing; Event (particle physics); Machine learning; Artificial neural network; Process (computing); Feature (linguistics); Artificial intelligence; Recurrent neural network; Code (set theory); World Wide Web; Programming language","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":[0.001805094,0.001581815,0.0006136692,0.002684175,0.0002878614,0.0008828944,0.0009600665,0.0009903337,0.0008029374],"category_scores_gemma":[0.01146343,0.00045109,0.000694639,0.001586072,0.0003893859,0.001088901,0.0006802658,0.002333079,0.001345703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008755449,"about_ca_system_score_gemma":0.0007157155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009923138,"about_ca_topic_score_gemma":0.0106925,"domain_scores_codex":[0.9987386,0.0003786753,0.0001046902,0.0003442873,0.0002972449,0.0001365537],"domain_scores_gemma":[0.9922252,0.004902744,0.0008083755,0.0006819511,0.0010893,0.000292518],"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":[0.0003521853,0.001033494,0.1369507,0.000209279,0.000184735,0.0003337115,0.0003157064,0.5501861,0.004015298,0.001025322,0.02360493,0.2817885],"study_design_scores_gemma":[0.000006821712,0.00004907367,0.00942126,0.000009687826,0.000007359096,0.00002627013,0.00003993441,0.9872617,0.001122116,0.001293514,0.000750314,0.00001198645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8183966,0.001345647,0.1614956,0.001247832,0.0002429949,0.0001858082,0.00580817,0.008801398,0.002475936],"genre_scores_gemma":[0.9412419,0.0002677783,0.04807334,0.00008164083,0.0001032762,0.0001148626,0.008388176,0.0001521717,0.001576889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009923138,"threshold_uncertainty_score":0.01973075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01321479887293015,"score_gpt":0.2339957733762227,"score_spread":0.2207809745032926,"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."}}