{"id":"W2620682708","doi":"10.1109/icse-c.2017.11","title":"RACK: Code Search in the IDE Using Crowdsourced Knowledge","year":2017,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Information retrieval; Web search query; Code (set theory); Context (archaeology); Source code; Programming language; Matching (statistics); Query expansion; World Wide Web; Search engine; Database; Set (abstract data type)","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":[0.001418642,0.00008334109,0.00009153644,0.0001172435,0.000319624,0.0007444662,0.003290182,0.00004126833,0.00001763567],"category_scores_gemma":[0.0008419206,0.00005797014,0.00003337638,0.0002118721,0.00007291929,0.0003764676,0.0007590146,0.0002723014,0.0001322424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000591916,"about_ca_system_score_gemma":0.0001041204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002643311,"about_ca_topic_score_gemma":0.00009117236,"domain_scores_codex":[0.9988405,0.00009126022,0.0001117453,0.0002509652,0.0003248151,0.0003807596],"domain_scores_gemma":[0.9976688,0.0007238429,0.00001940079,0.001463774,0.0000652556,0.00005890489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003816987,0.0007632994,0.5792201,0.0002735782,0.00009495064,0.0008413868,0.04440366,0.02019829,0.02539109,0.1371377,0.01285522,0.1787826],"study_design_scores_gemma":[0.0004983424,0.00003325039,0.3777231,0.00004398513,0.000001414632,0.00003855087,0.00009050263,0.6130055,0.004624832,0.0004463019,0.003268853,0.0002253626],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6134728,0.00007336335,0.3799092,0.0009928958,0.000183775,0.0001762425,4.522042e-7,0.0001343636,0.005057],"genre_scores_gemma":[0.9844654,0.000002439313,0.01466851,0.00004741719,0.00006788832,0.000006467408,1.45829e-7,0.000008377354,0.0007333271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5928072,"threshold_uncertainty_score":0.7178904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1014876312730591,"score_gpt":0.3820968922068171,"score_spread":0.2806092609337579,"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."}}