{"id":"W2138295189","doi":"10.1109/csmr.2008.4493326","title":"Visual Detection of Design Anomalies","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... European Conference on Software Maintenance and Reengineering/Proceedings of the European Conference on Software Maintenance and Reengineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Maintainability; Computer science; Anomaly detection; Flexibility (engineering); Visualization; Anomaly (physics); Task (project management); Data mining; Software; Artificial intelligence; Precision and recall; Machine learning; Software engineering; Engineering; Programming language; Systems engineering","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":[0.001654545,0.001105387,0.0005700632,0.004061441,0.0002577758,0.001491906,0.000845268,0.0009842943,0.003477553],"category_scores_gemma":[0.01183355,0.0003309932,0.0004082271,0.001155172,0.0002634538,0.00103882,0.001140386,0.0007814066,0.001041171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000382443,"about_ca_system_score_gemma":0.0004328557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001391821,"about_ca_topic_score_gemma":0.001264354,"domain_scores_codex":[0.9989265,0.0003024195,0.00006723963,0.0001817286,0.0004446725,0.00007740541],"domain_scores_gemma":[0.9908074,0.004312718,0.0009307652,0.001210091,0.002474647,0.0002645016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001104158,0.0002191485,0.02109604,0.001389094,0.0001643331,0.001204045,0.002682615,0.01423335,0.1554656,0.006773687,0.02982254,0.7658454],"study_design_scores_gemma":[0.0002856786,0.001064317,0.06877644,0.000747397,0.0003584104,0.005465447,0.00153025,0.5674264,0.2031732,0.0281507,0.1226872,0.0003346227],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1361653,0.001266889,0.8084343,0.0007555162,0.000198467,0.0004050707,0.00251741,0.03945933,0.01079761],"genre_scores_gemma":[0.5966461,0.0006464816,0.3934195,0.0002326997,0.0000959004,0.0002068799,0.002485576,0.001323886,0.004943045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004061441,"threshold_uncertainty_score":0.01163352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02462270969935319,"score_gpt":0.2174641217379797,"score_spread":0.1928414120386265,"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."}}