{"id":"W4384158949","doi":"10.1109/icpc58990.2023.00033","title":"UnityLint: A Bad Smell Detector for Unity","year":2023,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Code smell; Computer science; Metadata; Leverage (statistics); Video game; Software; Source code; Video game development; Domain (mathematical analysis); Human–computer interaction; Graphics; Software engineering; Open source; World Wide Web; Multimedia; Software development; Game design; Software quality; Artificial intelligence; Computer graphics (images); Programming language","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":[0.001698057,0.001665045,0.0006691269,0.006920361,0.0005214394,0.002091527,0.00131799,0.00143524,0.004651082],"category_scores_gemma":[0.0153484,0.0007298305,0.0009059833,0.001786418,0.0006804901,0.003165255,0.002454062,0.001056147,0.004591529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007409927,"about_ca_system_score_gemma":0.0009589859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002986021,"about_ca_topic_score_gemma":0.00477982,"domain_scores_codex":[0.9973763,0.0002610409,0.0003189656,0.0006254175,0.001250541,0.0001676208],"domain_scores_gemma":[0.9904108,0.003975803,0.002237967,0.001362253,0.001711271,0.0003019238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00133081,0.0003772141,0.1001843,0.002880419,0.0002849255,0.003339899,0.003091081,0.004436464,0.06117718,0.008529349,0.2028448,0.6115237],"study_design_scores_gemma":[0.00024313,0.0008576168,0.1261861,0.001307897,0.0003567103,0.007708639,0.00187399,0.2309752,0.2310462,0.02209744,0.3767276,0.0006195519],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"software","genre_gemma":"empirical","genre_scores_codex":[0.1482164,0.001952352,0.3603368,0.001133362,0.0004063553,0.0008810323,0.03281436,0.4366929,0.01756635],"genre_scores_gemma":[0.4671126,0.0009787756,0.4231815,0.001092807,0.0001124147,0.0008573949,0.06234542,0.0240302,0.0202889],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006920361,"threshold_uncertainty_score":0.01555938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04147912389474392,"score_gpt":0.3008631857958433,"score_spread":0.2593840619010994,"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."}}