{"id":"W2034400602","doi":"10.1109/quatic.2010.61","title":"IDS: An Immune-Inspired Approach for the Detection of Software Design Smells","year":2010,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Université de Montréal","funders":"","keywords":"Code smell; Computer science; Creatures; Software engineering; Software; Artificial intelligence; Human–computer interaction; Software quality; Programming language; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009174095,0.0000926534,0.0001040553,0.00008369395,0.0001140469,0.0000876105,0.001176619,0.00007472269,0.000006533766],"category_scores_gemma":[0.0007328625,0.0000629308,0.00006106534,0.0003153768,0.00004977125,0.0002881091,0.0001186202,0.0001897359,0.000004616085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001354671,"about_ca_system_score_gemma":0.00005570174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005747402,"about_ca_topic_score_gemma":0.000004779689,"domain_scores_codex":[0.999081,0.00003563525,0.0001576512,0.0002467706,0.0002435846,0.0002353229],"domain_scores_gemma":[0.9975798,0.001312977,0.00003829171,0.0008627928,0.0001491078,0.00005702005],"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":[0.00004199676,0.0002197718,0.0007312058,0.00009141172,0.00006302824,9.684065e-7,0.0008999704,0.01219132,0.4683117,0.003854089,0.0001577887,0.5134367],"study_design_scores_gemma":[0.0002831784,0.0002280148,0.006746154,0.000002859279,0.000004952396,0.000008930913,0.00002537602,0.63807,0.353721,0.0004435059,0.0003274062,0.000138703],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007442583,0.00006131054,0.9912871,0.0000331072,0.0003030474,0.0004987041,8.741986e-7,0.0003532838,0.00001997198],"genre_scores_gemma":[0.5692131,0.000001531955,0.4305777,0.000009955299,0.00003733194,0.00007608438,5.949416e-7,0.000009277855,0.00007445887],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6258786,"threshold_uncertainty_score":0.2566243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03121119965723664,"score_gpt":0.2650303641087586,"score_spread":0.233819164451522,"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."}}