{"id":"W2911789761","doi":"10.1007/s10664-018-9671-0","title":"Automatic query reformulation for code search using crowdsourced knowledge","year":2019,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Information retrieval; Web search query; Java; Code (set theory); Query expansion; Search engine; Web query classification; XPath; Precision and recall; Natural language; Programming language; World Wide Web; Artificial intelligence; XML; 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":[0.002599543,0.001497873,0.001869257,0.007863757,0.002171537,0.002677264,0.002772435,0.002239003,0.0143394],"category_scores_gemma":[0.0238045,0.0005852267,0.001697311,0.004772567,0.001071148,0.004800716,0.005934762,0.001662196,0.006777961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001842096,"about_ca_system_score_gemma":0.004259887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01481199,"about_ca_topic_score_gemma":0.0202161,"domain_scores_codex":[0.9929793,0.001867073,0.0005192513,0.001220071,0.002882875,0.000531339],"domain_scores_gemma":[0.9879003,0.005119511,0.0005158175,0.002669304,0.003428825,0.0003663379],"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":[0.001076918,0.0007961448,0.003321392,0.001711614,0.000195505,0.0007083854,0.002284469,0.01832168,0.05665681,0.01691444,0.110446,0.7875666],"study_design_scores_gemma":[0.0003655837,0.0004220522,0.004428033,0.0003410807,0.0003165897,0.0007783648,0.003808708,0.7850179,0.06859254,0.05432808,0.08137117,0.0002298267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08637204,0.002182651,0.8493398,0.002100178,0.0005065381,0.001737764,0.009045261,0.0349112,0.0138045],"genre_scores_gemma":[0.4213272,0.0007229435,0.5393496,0.0006939675,0.0002836795,0.0009966008,0.025087,0.002061641,0.009477247],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01481199,"threshold_uncertainty_score":0.04797012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0489429157531969,"score_gpt":0.3347416356903026,"score_spread":0.2857987199371057,"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."}}