{"id":"W2600957813","doi":"10.1109/saner.2017.7884630","title":"An empirical study of code smells in JavaScript projects","year":2017,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"JavaScript; Computer science; Code smell; Programming language; Code (set theory); Empirical research; World Wide Web; Computer security; Software quality; Software; Software development","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":[0.006786933,0.0003110992,0.0002695758,0.002697513,0.0005612655,0.001335073,0.0006709641,0.0008189166,0.001155316],"category_scores_gemma":[0.08614527,0.0003527147,0.0004323631,0.002108834,0.001026089,0.002234084,0.001225555,0.001300551,0.0004096497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009025467,"about_ca_system_score_gemma":0.0008533283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003609005,"about_ca_topic_score_gemma":0.003994928,"domain_scores_codex":[0.9934444,0.001784147,0.0008180048,0.000796684,0.002533336,0.000623428],"domain_scores_gemma":[0.7857242,0.1029863,0.08164015,0.005522895,0.01793958,0.006186782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008898794,0.0001369539,0.9863887,0.00007399647,0.00002798293,0.0001933146,0.004456493,0.0001995758,0.0003998422,0.00006487069,0.0002953514,0.007673812],"study_design_scores_gemma":[0.000004304253,0.0002084023,0.9922999,0.00005503515,0.00001345599,0.0002352942,0.004907686,0.001211129,0.0003028475,0.00006853652,0.0006742498,0.00001919025],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988496,0.0001040579,0.0003747559,0.00008787788,0.000003149,0.00002324592,0.0001529518,0.00001557686,0.0003887262],"genre_scores_gemma":[0.9990637,0.00008825419,0.0003443007,0.00002738194,0.000005998676,0.00002843472,0.0002376896,0.00001231976,0.0001920071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006786933,"threshold_uncertainty_score":0.03589314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08580029150157727,"score_gpt":0.3896327072934044,"score_spread":0.3038324157918272,"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."}}