{"id":"W3183042663","doi":"10.1109/icsme52107.2021.00036","title":"Design Smells in Deep Learning Programs: An Empirical Study","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Code smell; Computer science; Context (archaeology); Artificial intelligence; Artificial neural network; Relevance (law); Empirical research; Software engineering; Software; Quality (philosophy); Machine learning; Software quality; 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":[0.01320387,0.0005322062,0.0004211911,0.002758102,0.0006771868,0.001639065,0.001157227,0.001288061,0.001629627],"category_scores_gemma":[0.1025916,0.00063198,0.0005946981,0.002352198,0.001520264,0.003668171,0.001894972,0.002206737,0.0004603963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001373666,"about_ca_system_score_gemma":0.001076696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001936921,"about_ca_topic_score_gemma":0.003150476,"domain_scores_codex":[0.988637,0.003518896,0.001363797,0.0009262365,0.004957326,0.0005967243],"domain_scores_gemma":[0.7752295,0.1535328,0.04453902,0.005876087,0.01742402,0.003398635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005581701,0.002089809,0.8831021,0.0009792449,0.0001178947,0.001526571,0.0243997,0.00227094,0.002550925,0.0007006942,0.002252373,0.07945164],"study_design_scores_gemma":[0.0000894875,0.002544821,0.9129463,0.001087327,0.0001536177,0.002785516,0.03320633,0.02913373,0.005800065,0.001202296,0.01089539,0.0001551208],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979669,0.0002705283,0.000935896,0.0001176479,0.000003575926,0.00004457751,0.00007914196,0.00003957994,0.0005420785],"genre_scores_gemma":[0.9971166,0.0002737295,0.001721446,0.00008652974,0.000005842532,0.00006613909,0.0002642107,0.00003121506,0.0004343499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01320387,"threshold_uncertainty_score":0.06982952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08701109926562463,"score_gpt":0.3627289947378883,"score_spread":0.2757178954722637,"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."}}