{"id":"W2754109047","doi":"10.1109/tse.2017.2750682","title":"Expanding Queries for Code Search Using Semantically Related API Class-names","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Identifier; Programming language; Class (philosophy); Information retrieval; Natural language; Java; Code (set theory); Natural language user interface; World Wide Web; Natural language processing; Artificial intelligence","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":[0.00262719,0.002512382,0.002023084,0.009228573,0.001300721,0.002266065,0.001906298,0.00225027,0.003736472],"category_scores_gemma":[0.01895829,0.0008667045,0.001996159,0.004902173,0.001172345,0.006116499,0.003577225,0.001843667,0.00258046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001478537,"about_ca_system_score_gemma":0.002924963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0110534,"about_ca_topic_score_gemma":0.01491992,"domain_scores_codex":[0.9945354,0.001416206,0.0006438476,0.001273545,0.001782157,0.0003487196],"domain_scores_gemma":[0.9882048,0.007461459,0.0008247712,0.001152103,0.001986984,0.0003698331],"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":[0.00150959,0.001473265,0.03704362,0.004544456,0.0003518547,0.00331353,0.008927234,0.01869737,0.1182436,0.01374401,0.06959958,0.7225519],"study_design_scores_gemma":[0.0004776599,0.001139898,0.03068458,0.0007037308,0.0006411478,0.006442041,0.00820321,0.6208719,0.07731723,0.03078446,0.2222944,0.0004397406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2974809,0.005445616,0.5843075,0.003829421,0.0003425226,0.002851685,0.01841281,0.07399307,0.01333656],"genre_scores_gemma":[0.2913313,0.001268299,0.6688223,0.001077013,0.0001616049,0.0007812011,0.03053277,0.002039436,0.003986091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0110534,"threshold_uncertainty_score":0.02197814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03992796610891364,"score_gpt":0.3092392162335377,"score_spread":0.2693112501246241,"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."}}