{"id":"W1975143584","doi":"10.1109/wcre.2007.27","title":"Examining the Effects of Global Data Usage on Software Maintainability","year":2007,"lang":"en","type":"article","venue":"Proceedings - Working Conference on Reverse Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Maintainability; Computer science; Software maintenance; Source code; Software; Software evolution; Variable (mathematics); Source lines of code; Task (project management); Software metric; Software development; Software engineering; Software construction; Operating system; Systems engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002262407,0.0003468856,0.000321149,0.0001651755,0.0001391472,0.000240222,0.003092343,0.0001316027,0.000005002168],"category_scores_gemma":[0.007551095,0.0002931495,0.00005913149,0.001255477,0.00007909133,0.0003986871,0.001069389,0.0006622117,0.00001413952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003399869,"about_ca_system_score_gemma":0.00009829232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000180378,"about_ca_topic_score_gemma":0.000001458522,"domain_scores_codex":[0.9971026,0.00001673665,0.0003846339,0.000850861,0.0008372596,0.0008078844],"domain_scores_gemma":[0.9962676,0.002040996,0.0001443627,0.001165946,0.0001900661,0.0001909847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001763259,0.0004485941,0.3602753,0.002977516,0.0003331305,0.000324814,0.002975781,0.004690546,0.007706849,0.3920078,0.001664259,0.2264191],"study_design_scores_gemma":[0.002376918,0.001743359,0.7430359,0.0087215,0.00009681247,0.000131939,0.000672618,0.2128767,0.01763795,0.002702043,0.007289848,0.002714455],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4871111,0.0001018947,0.509217,0.0002010864,0.001069809,0.0006524768,0.000004158488,0.0009176753,0.0007249054],"genre_scores_gemma":[0.9717863,0.00001467917,0.02792007,0.00006756256,0.0001335639,0.00002064521,0.000002020326,0.00002739324,0.00002781378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4846752,"threshold_uncertainty_score":0.9999521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04660345742979925,"score_gpt":0.2815427781236253,"score_spread":0.2349393206938261,"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."}}