{"id":"W2126730858","doi":"10.1145/1806799.1806846","title":"Identifying crosscutting concerns using historical code changes","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":"Queen's University","funders":"","keywords":"Commit; Merge (version control); Computer science; Source code; Data science; Code review; Code (set theory); Complement (music); Open source; Software engineering; Static program analysis; Risk analysis (engineering); Programming language; Software; Software development; Database; Information retrieval; Business","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":[0.001967895,0.0007149554,0.0003803096,0.005561392,0.000517286,0.001128587,0.001018088,0.0007536479,0.000932382],"category_scores_gemma":[0.01929161,0.0004955187,0.00062868,0.00282226,0.0003850141,0.002288324,0.0009231292,0.0008548786,0.0004634364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005388313,"about_ca_system_score_gemma":0.0007385412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004275465,"about_ca_topic_score_gemma":0.01056696,"domain_scores_codex":[0.9976776,0.0003130116,0.000188119,0.0007628623,0.0009346165,0.0001237903],"domain_scores_gemma":[0.9790763,0.007978148,0.004228411,0.004099166,0.004156476,0.0004614807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002349373,0.0002355534,0.3186213,0.0005909275,0.0001749062,0.00128315,0.002778444,0.009087708,0.03795897,0.002376458,0.002942893,0.6237147],"study_design_scores_gemma":[0.000044415,0.0006887542,0.5954215,0.0002809128,0.0004722038,0.004066315,0.001580062,0.2698835,0.08014212,0.007244264,0.04000575,0.0001701346],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8049076,0.001426762,0.1793048,0.0003128137,0.00009754541,0.0003194502,0.002590085,0.005844021,0.005196937],"genre_scores_gemma":[0.8502864,0.0007743565,0.140043,0.00006295909,0.0000503099,0.0001346399,0.005541875,0.000531112,0.002575348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005561392,"threshold_uncertainty_score":0.01040733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1026492504951821,"score_gpt":0.355441768671808,"score_spread":0.252792518176626,"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."}}