{"id":"W2953520354","doi":"10.1109/icse.2019.00032","title":"DLFinder: Characterizing and Detecting Duplicate Logging Code Smells","year":2019,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Program comprehension; Debugging; Logging; Code smell; Code (set theory); Source code; Software; Software engineering; Database; Software development; Software quality; Software system; 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":[0.006538116,0.001877214,0.0008631114,0.00885578,0.000944412,0.00159882,0.002545765,0.001250217,0.001140503],"category_scores_gemma":[0.04616608,0.001230054,0.0007756275,0.00323289,0.001393569,0.004111349,0.002848372,0.001732367,0.001500027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001181427,"about_ca_system_score_gemma":0.002511185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005183026,"about_ca_topic_score_gemma":0.009733526,"domain_scores_codex":[0.9895346,0.001531606,0.001185585,0.002301904,0.004887767,0.0005585793],"domain_scores_gemma":[0.9413413,0.0254347,0.01137406,0.009950276,0.01080061,0.001099023],"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":[0.001102347,0.0007845803,0.2774361,0.003445866,0.0003318973,0.003380197,0.01009012,0.009112355,0.0677315,0.003066555,0.07578522,0.5477332],"study_design_scores_gemma":[0.0004929456,0.00129528,0.276837,0.001123915,0.0004912957,0.005922155,0.005409798,0.3707936,0.1933266,0.01074125,0.1327332,0.0008329928],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.575652,0.002550815,0.1641059,0.001056444,0.0002877233,0.0007506831,0.01214741,0.237923,0.005525963],"genre_scores_gemma":[0.7255359,0.0006643718,0.2355624,0.0006518461,0.00008628992,0.0007436064,0.02093133,0.01077899,0.005045299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00885578,"threshold_uncertainty_score":0.03457731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01051164114387913,"score_gpt":0.2236722693725866,"score_spread":0.2131606282287074,"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."}}